From 7f868d9903c811188601cf81923161dfe4854c9e Mon Sep 17 00:00:00 2001 From: Peter Williams <105436708+pewill-msft@users.noreply.github.com> Date: Thu, 10 Sep 2026 15:10:34 +0200 Subject: [PATCH 01/18] Add devcontainer configuration for predictive maintenance --- .../devcontainer.json | 62 +++++++++++++++++++ 1 file changed, 62 insertions(+) create mode 100644 .devcontainer/03-azure-01-04-ai-08-predictive-maintenance/devcontainer.json diff --git a/.devcontainer/03-azure-01-04-ai-08-predictive-maintenance/devcontainer.json b/.devcontainer/03-azure-01-04-ai-08-predictive-maintenance/devcontainer.json new file mode 100644 index 000000000..32fcafad9 --- /dev/null +++ b/.devcontainer/03-azure-01-04-ai-08-predictive-maintenance/devcontainer.json @@ -0,0 +1,62 @@ +{ + "name": "Python 3", + "build": { + "dockerfile": "Dockerfile" + }, + "forwardPorts": [ + 5231 + ], + "portsAttributes": { + "5231": { + "label": "workflow-api-http", + "visibility": "public", + "protocol": "http" + } + }, + "features": { + "ghcr.io/devcontainers/features/azure-cli:1": { + "installBicep": true + }, + "ghcr.io/devcontainer-community/devcontainer-features/astral.sh-uv:1": {}, + "ghcr.io/devcontainers/features/docker-in-docker:2": { + "version": "latest", + "enableNonRootDocker": "true" + }, + "ghcr.io/devcontainers/features/node:1": { + "version": "lts" + }, + "ghcr.io/devcontainers/features/dotnet:2": { + "version": "10.0", + "installUsingApt": true + } + }, + "workspaceMount": "source=${localWorkspaceFolder},target=/workspaces/microhack,type=bind,consistency=cached", + "workspaceFolder": "/workspaces/microhack/03-Azure/01-04-AI/08_Predictive_Maintenance", + "postAttachCommand": "bash /workspaces/microhack/.devcontainer/welcome.sh", + "remoteUser": "vscode", + //"postCreateCommand": "pip3 install --upgrade pip && pip3 install --user -r requirements.txt && bash /workspaces/microhack/.devcontainer/03-azure-01-04-ai-08-predictive-maintenance/setup-dotnet.sh", + "customizations": { + "vscode": { + "extensions": [ + "github.copilot-chat", + "github.github-vscode-theme", + "kejun.markdown-alert", + "ms-python.debugpy", + "ms-python.python", + "ms-python.vscode-pylance", + "ms-dotnettools.csdevkit", + "ms-dotnettools.csharp", + "ms-dotnettools.vscode-dotnet-runtime", + "ms-toolsai.jupyter", + "ms-toolsai.jupyter-renderers", + "ms-azuretools.vscode-bicep", + "ms-windows-ai-studio.windows-ai-studio", + "humao.rest-client" + + ], + "settings": { + "workbench.colorTheme": "GitHub Dark Default" + } + } + } +} From fbe489016d444504cafe8f6a54b76c2e7b1d9806 Mon Sep 17 00:00:00 2001 From: Peter Williams <105436708+pewill-msft@users.noreply.github.com> Date: Thu, 10 Sep 2026 15:11:43 +0200 Subject: [PATCH 02/18] Update Dockerfile to remove Yarn apt repo Remove Yarn apt repository to prevent expired GPG key errors during installations. --- .../03-azure-01-04-ai-08-predictive-maintenance/Dockerfile | 6 ++++++ 1 file changed, 6 insertions(+) create mode 100644 .devcontainer/03-azure-01-04-ai-08-predictive-maintenance/Dockerfile diff --git a/.devcontainer/03-azure-01-04-ai-08-predictive-maintenance/Dockerfile b/.devcontainer/03-azure-01-04-ai-08-predictive-maintenance/Dockerfile new file mode 100644 index 000000000..d80709b7c --- /dev/null +++ b/.devcontainer/03-azure-01-04-ai-08-predictive-maintenance/Dockerfile @@ -0,0 +1,6 @@ +FROM mcr.microsoft.com/devcontainers/python:1-3.11-bookworm + +# Remove Yarn apt repo entirely to avoid expired GPG key errors during feature installs +USER root +RUN rm -f /etc/apt/sources.list.d/yarn.list \ + && rm -f /usr/share/keyrings/yarn.gpg From afebbc3edf8247c6e5fbb837ba695d754a88cd8f Mon Sep 17 00:00:00 2001 From: Peter Williams <105436708+pewill-msft@users.noreply.github.com> Date: Thu, 10 Sep 2026 15:13:26 +0200 Subject: [PATCH 03/18] Create Predictive Maintenance Root folder --- 03-Azure/08_Predictive_Maintenance/README.md | 1 + 1 file changed, 1 insertion(+) create mode 100644 03-Azure/08_Predictive_Maintenance/README.md diff --git a/03-Azure/08_Predictive_Maintenance/README.md b/03-Azure/08_Predictive_Maintenance/README.md new file mode 100644 index 000000000..8b1378917 --- /dev/null +++ b/03-Azure/08_Predictive_Maintenance/README.md @@ -0,0 +1 @@ + From 24a91cb93fd60c2634ab0836d656b122b72ab7da Mon Sep 17 00:00:00 2001 From: Peter Williams <105436708+pewill-msft@users.noreply.github.com> Date: Thu, 10 Sep 2026 15:19:07 +0200 Subject: [PATCH 04/18] Rename devcontainer to Azure / AI / Predictive Maintenance --- .../devcontainer.json | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.devcontainer/03-azure-01-04-ai-08-predictive-maintenance/devcontainer.json b/.devcontainer/03-azure-01-04-ai-08-predictive-maintenance/devcontainer.json index 32fcafad9..0420f494f 100644 --- a/.devcontainer/03-azure-01-04-ai-08-predictive-maintenance/devcontainer.json +++ b/.devcontainer/03-azure-01-04-ai-08-predictive-maintenance/devcontainer.json @@ -1,5 +1,5 @@ { - "name": "Python 3", + "name": "Azure / AI / Predictive Maintenance", "build": { "dockerfile": "Dockerfile" }, From f722e91f116b276624b35339f1d4737f657444d4 Mon Sep 17 00:00:00 2001 From: Peter Williams <105436708+pewill-msft@users.noreply.github.com> Date: Thu, 10 Sep 2026 15:53:31 +0200 Subject: [PATCH 05/18] Add README.md with TODO note for predictive maintenance --- 03-Azure/01-04-AI/08_Predictive_Maintenance/README.md | 1 + 1 file changed, 1 insertion(+) create mode 100644 03-Azure/01-04-AI/08_Predictive_Maintenance/README.md diff --git a/03-Azure/01-04-AI/08_Predictive_Maintenance/README.md b/03-Azure/01-04-AI/08_Predictive_Maintenance/README.md new file mode 100644 index 000000000..1333ed77b --- /dev/null +++ b/03-Azure/01-04-AI/08_Predictive_Maintenance/README.md @@ -0,0 +1 @@ +TODO From 0a737427494c75faef546e01465da2b80a03fdf4 Mon Sep 17 00:00:00 2001 From: Peter Williams <105436708+pewill-msft@users.noreply.github.com> Date: Thu, 10 Sep 2026 15:54:11 +0200 Subject: [PATCH 06/18] Delete 03-Azure/08_Predictive_Maintenance/README.md --- 03-Azure/08_Predictive_Maintenance/README.md | 1 - 1 file changed, 1 deletion(-) delete mode 100644 03-Azure/08_Predictive_Maintenance/README.md diff --git a/03-Azure/08_Predictive_Maintenance/README.md b/03-Azure/08_Predictive_Maintenance/README.md deleted file mode 100644 index 8b1378917..000000000 --- a/03-Azure/08_Predictive_Maintenance/README.md +++ /dev/null @@ -1 +0,0 @@ - From 9c946d806ebd61ca15374f514e84c7f854a208dd Mon Sep 17 00:00:00 2001 From: Peter Williams <105436708+pewill-msft@users.noreply.github.com> Date: Thu, 10 Sep 2026 16:44:38 +0200 Subject: [PATCH 07/18] Update devcontainer.json for workspace configuration --- .../devcontainer.json | 9 ++++----- 1 file changed, 4 insertions(+), 5 deletions(-) diff --git a/.devcontainer/03-azure-01-04-ai-08-predictive-maintenance/devcontainer.json b/.devcontainer/03-azure-01-04-ai-08-predictive-maintenance/devcontainer.json index 0420f494f..ea906b20d 100644 --- a/.devcontainer/03-azure-01-04-ai-08-predictive-maintenance/devcontainer.json +++ b/.devcontainer/03-azure-01-04-ai-08-predictive-maintenance/devcontainer.json @@ -30,11 +30,10 @@ "installUsingApt": true } }, - "workspaceMount": "source=${localWorkspaceFolder},target=/workspaces/microhack,type=bind,consistency=cached", - "workspaceFolder": "/workspaces/microhack/03-Azure/01-04-AI/08_Predictive_Maintenance", - "postAttachCommand": "bash /workspaces/microhack/.devcontainer/welcome.sh", - "remoteUser": "vscode", - //"postCreateCommand": "pip3 install --upgrade pip && pip3 install --user -r requirements.txt && bash /workspaces/microhack/.devcontainer/03-azure-01-04-ai-08-predictive-maintenance/setup-dotnet.sh", + "workspaceFolder": "/workspaces/${localWorkspaceFolderBasename}/03-Azure/01-04-AI/08_Predictive_Maintenance", + "postAttachCommand": "bash /workspaces/${localWorkspaceFolderBasename}/.devcontainer/welcome.sh", + "remoteUser": "vscode", + // "postCreateCommand": "pip3 install --upgrade pip && pip3 install --user -r requirements.txt && bash /workspaces/${localWorkspaceFolderBasename}/.devcontainer/03-azure-01-04-ai-08-predictive-maintenance/setup-dotnet.sh", "customizations": { "vscode": { "extensions": [ From 88da937880ea32f229e2ec1f91acddb0c6479bf5 Mon Sep 17 00:00:00 2001 From: Peter Williams <105436708+pewill-msft@users.noreply.github.com> Date: Thu, 10 Sep 2026 14:53:58 +0000 Subject: [PATCH 08/18] Add git configuration to devcontainer for opening repositories in parent folders --- .../devcontainer.json | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/.devcontainer/03-azure-01-04-ai-08-predictive-maintenance/devcontainer.json b/.devcontainer/03-azure-01-04-ai-08-predictive-maintenance/devcontainer.json index ea906b20d..1c5ea4a8c 100644 --- a/.devcontainer/03-azure-01-04-ai-08-predictive-maintenance/devcontainer.json +++ b/.devcontainer/03-azure-01-04-ai-08-predictive-maintenance/devcontainer.json @@ -54,7 +54,8 @@ ], "settings": { - "workbench.colorTheme": "GitHub Dark Default" + "workbench.colorTheme": "GitHub Dark Default", + "git.openRepositoryInParentFolders": "always" } } } From deea3a4875ae88dfa17081b16825962d93c775cb Mon Sep 17 00:00:00 2001 From: Peter Williams <105436708+pewill-msft@users.noreply.github.com> Date: Fri, 11 Sep 2026 19:45:08 +0000 Subject: [PATCH 09/18] Draft challenges 01-05, finish page, and devcontainer setup for Predictive Maintenance hack - Add challenges/challenge-01.md through challenge-05.md, converted from the original agentic-factory-hack challenge-0..4 READMEs, renumbered and restructured to fit the MicroHack challenge/walkthrough split (long sample output moved out to future walkthrough docs, code paths updated to the planned src/ layout). - Add challenges/finish.md wrap-up page. - Reuse all original hand-drawn diagrams/screenshots from the source repo in challenges/images/ (no AI-generated diagrams). - Update README.md to use the real diagrams instead of mermaid. - Add .github/copilot-instructions.md and .github/agents/agentplanning.agent.md carried over from the source repo, updated for the new numbering/paths. - Fix devcontainer.json postCreateCommand and add setup-dotnet.sh for the root-level devcontainer selector. Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> --- .../devcontainer.json | 7 +- .../setup-dotnet.sh | 12 + .../.github/agents/agentplanning.agent.md | 305 ++++++++++++++ .../.github/copilot-instructions.md | 72 ++++ .../08_Predictive_Maintenance/README.md | 309 +++++++++++++- .../challenges/challenge-01.md | 165 ++++++++ .../challenges/challenge-02.md | 384 ++++++++++++++++++ .../challenges/challenge-03.md | 382 +++++++++++++++++ .../challenges/challenge-04.md | 277 +++++++++++++ .../challenges/challenge-05.md | 352 ++++++++++++++++ .../challenges/finish.md | 9 + .../agentic-maintenance-orchestration.png | Bin 0 -> 480416 bytes .../challenge-01-azure-portal-resources.png | Bin 0 -> 215926 bytes .../images/challenge-01-azure-resources.png | Bin 0 -> 305747 bytes .../images/challenge-02-ai-gateway.png | Bin 0 -> 213774 bytes .../images/challenge-02-azure-resources.png | Bin 0 -> 322937 bytes .../images/challenge-02-foundry-iq.png | Bin 0 -> 245982 bytes ...ry-portal-anomaly-classification-agent.png | Bin 0 -> 105243 bytes .../challenge-02-foundry-portal-build-tab.png | Bin 0 -> 6790 bytes ...challenge-02-foundry-portal-playground.png | Bin 0 -> 257042 bytes .../images/challenge-02-mcp-url.png | Bin 0 -> 20657 bytes .../challenges/images/challenge-02-mcp.png | Bin 0 -> 312988 bytes .../challenge-02-new-foundry-portal.png | Bin 0 -> 29562 bytes .../images/challenge-02-portal-apis.png | Bin 0 -> 19815 bytes .../images/challenge-02-portal-expose-mcp.png | Bin 0 -> 86581 bytes .../images/challenge-02-scenario.png | Bin 0 -> 474371 bytes ...challenge-02-select-python-environment.png | Bin 0 -> 17056 bytes .../challenges/images/challenge-02-task-1.png | Bin 0 -> 105940 bytes .../challenges/images/challenge-02-task-2.png | Bin 0 -> 165574 bytes .../challenges/images/challenge-02-task-3.png | Bin 0 -> 195798 bytes .../images/challenge-02-wiki-storage-page.png | Bin 0 -> 374717 bytes .../challenge-03-agentplanning-selection.png | Bin 0 -> 42103 bytes .../challenge-03-copilot-instructions.png | Bin 0 -> 222936 bytes .../images/challenge-03-scenario.png | Bin 0 -> 438029 bytes .../images/challenge-03-target-solution.png | Bin 0 -> 109517 bytes .../challenge-04-application-insights.png | Bin 0 -> 109330 bytes .../images/challenge-04-azure-resources.png | Bin 0 -> 323224 bytes .../images/challenge-04-monitor-portal.png | Bin 0 -> 215383 bytes .../images/challenge-04-open-in-monitor.png | Bin 0 -> 9831 bytes .../images/challenge-04-scenario.png | Bin 0 -> 477469 bytes .../challenges/images/challenge-04-task-1.png | Bin 0 -> 264409 bytes .../challenges/images/challenge-04-task-2.png | Bin 0 -> 261498 bytes .../challenges/images/challenge-04-task-3.png | Bin 0 -> 182090 bytes .../challenge-05-agent-workflow-ui-end.png | Bin 0 -> 433477 bytes ...llenge-05-agent-workflow-ui-inprogress.png | Bin 0 -> 502173 bytes .../challenge-05-agent-workflow-ui-start.png | Bin 0 -> 561995 bytes ...hallenge-05-agent-workflow-ui-toolcall.png | Bin 0 -> 137178 bytes .../images/challenge-05-azure-resources.png | Bin 0 -> 314028 bytes .../images/challenge-05-dashboard-console.png | Bin 0 -> 506669 bytes .../images/challenge-05-dashboard-link.png | Bin 0 -> 76921 bytes .../challenge-05-dashboard-resources.png | Bin 0 -> 154910 bytes .../images/challenge-05-dashboard-traces.png | Bin 0 -> 272587 bytes .../images/challenge-05-frontend-link.png | Bin 0 -> 76903 bytes .../challenges/images/challenge-05-ports.png | Bin 0 -> 160400 bytes .../images/challenge-05-scenario.png | Bin 0 -> 470986 bytes .../challenge-05-workflow-architecture.png | Bin 0 -> 461602 bytes .../images/dataexplorer-sample-query.png | Bin 0 -> 88840 bytes .../images/hackathon-architecture.png | Bin 0 -> 381950 bytes .../images/maintenance-scenario.png | Bin 0 -> 427676 bytes 59 files changed, 2271 insertions(+), 3 deletions(-) create mode 100644 .devcontainer/03-azure-01-04-ai-08-predictive-maintenance/setup-dotnet.sh create mode 100644 03-Azure/01-04-AI/08_Predictive_Maintenance/.github/agents/agentplanning.agent.md create mode 100644 03-Azure/01-04-AI/08_Predictive_Maintenance/.github/copilot-instructions.md create mode 100644 03-Azure/01-04-AI/08_Predictive_Maintenance/challenges/challenge-01.md create mode 100644 03-Azure/01-04-AI/08_Predictive_Maintenance/challenges/challenge-02.md create mode 100644 03-Azure/01-04-AI/08_Predictive_Maintenance/challenges/challenge-03.md create mode 100644 03-Azure/01-04-AI/08_Predictive_Maintenance/challenges/challenge-04.md create mode 100644 03-Azure/01-04-AI/08_Predictive_Maintenance/challenges/challenge-05.md create mode 100644 03-Azure/01-04-AI/08_Predictive_Maintenance/challenges/finish.md create mode 100644 03-Azure/01-04-AI/08_Predictive_Maintenance/challenges/images/agentic-maintenance-orchestration.png create mode 100644 03-Azure/01-04-AI/08_Predictive_Maintenance/challenges/images/challenge-01-azure-portal-resources.png create mode 100644 03-Azure/01-04-AI/08_Predictive_Maintenance/challenges/images/challenge-01-azure-resources.png create mode 100644 03-Azure/01-04-AI/08_Predictive_Maintenance/challenges/images/challenge-02-ai-gateway.png create mode 100644 03-Azure/01-04-AI/08_Predictive_Maintenance/challenges/images/challenge-02-azure-resources.png create mode 100644 03-Azure/01-04-AI/08_Predictive_Maintenance/challenges/images/challenge-02-foundry-iq.png create mode 100644 03-Azure/01-04-AI/08_Predictive_Maintenance/challenges/images/challenge-02-foundry-portal-anomaly-classification-agent.png create mode 100644 03-Azure/01-04-AI/08_Predictive_Maintenance/challenges/images/challenge-02-foundry-portal-build-tab.png create mode 100644 03-Azure/01-04-AI/08_Predictive_Maintenance/challenges/images/challenge-02-foundry-portal-playground.png create mode 100644 03-Azure/01-04-AI/08_Predictive_Maintenance/challenges/images/challenge-02-mcp-url.png create mode 100644 03-Azure/01-04-AI/08_Predictive_Maintenance/challenges/images/challenge-02-mcp.png create mode 100644 03-Azure/01-04-AI/08_Predictive_Maintenance/challenges/images/challenge-02-new-foundry-portal.png create mode 100644 03-Azure/01-04-AI/08_Predictive_Maintenance/challenges/images/challenge-02-portal-apis.png create mode 100644 03-Azure/01-04-AI/08_Predictive_Maintenance/challenges/images/challenge-02-portal-expose-mcp.png create mode 100644 03-Azure/01-04-AI/08_Predictive_Maintenance/challenges/images/challenge-02-scenario.png create mode 100644 03-Azure/01-04-AI/08_Predictive_Maintenance/challenges/images/challenge-02-select-python-environment.png create mode 100644 03-Azure/01-04-AI/08_Predictive_Maintenance/challenges/images/challenge-02-task-1.png create mode 100644 03-Azure/01-04-AI/08_Predictive_Maintenance/challenges/images/challenge-02-task-2.png create mode 100644 03-Azure/01-04-AI/08_Predictive_Maintenance/challenges/images/challenge-02-task-3.png create mode 100644 03-Azure/01-04-AI/08_Predictive_Maintenance/challenges/images/challenge-02-wiki-storage-page.png create mode 100644 03-Azure/01-04-AI/08_Predictive_Maintenance/challenges/images/challenge-03-agentplanning-selection.png create mode 100644 03-Azure/01-04-AI/08_Predictive_Maintenance/challenges/images/challenge-03-copilot-instructions.png create mode 100644 03-Azure/01-04-AI/08_Predictive_Maintenance/challenges/images/challenge-03-scenario.png create mode 100644 03-Azure/01-04-AI/08_Predictive_Maintenance/challenges/images/challenge-03-target-solution.png create mode 100644 03-Azure/01-04-AI/08_Predictive_Maintenance/challenges/images/challenge-04-application-insights.png create mode 100644 03-Azure/01-04-AI/08_Predictive_Maintenance/challenges/images/challenge-04-azure-resources.png create mode 100644 03-Azure/01-04-AI/08_Predictive_Maintenance/challenges/images/challenge-04-monitor-portal.png create mode 100644 03-Azure/01-04-AI/08_Predictive_Maintenance/challenges/images/challenge-04-open-in-monitor.png create mode 100644 03-Azure/01-04-AI/08_Predictive_Maintenance/challenges/images/challenge-04-scenario.png create mode 100644 03-Azure/01-04-AI/08_Predictive_Maintenance/challenges/images/challenge-04-task-1.png create mode 100644 03-Azure/01-04-AI/08_Predictive_Maintenance/challenges/images/challenge-04-task-2.png create mode 100644 03-Azure/01-04-AI/08_Predictive_Maintenance/challenges/images/challenge-04-task-3.png create mode 100644 03-Azure/01-04-AI/08_Predictive_Maintenance/challenges/images/challenge-05-agent-workflow-ui-end.png create mode 100644 03-Azure/01-04-AI/08_Predictive_Maintenance/challenges/images/challenge-05-agent-workflow-ui-inprogress.png create mode 100644 03-Azure/01-04-AI/08_Predictive_Maintenance/challenges/images/challenge-05-agent-workflow-ui-start.png create mode 100644 03-Azure/01-04-AI/08_Predictive_Maintenance/challenges/images/challenge-05-agent-workflow-ui-toolcall.png create mode 100644 03-Azure/01-04-AI/08_Predictive_Maintenance/challenges/images/challenge-05-azure-resources.png create mode 100644 03-Azure/01-04-AI/08_Predictive_Maintenance/challenges/images/challenge-05-dashboard-console.png create mode 100644 03-Azure/01-04-AI/08_Predictive_Maintenance/challenges/images/challenge-05-dashboard-link.png create mode 100644 03-Azure/01-04-AI/08_Predictive_Maintenance/challenges/images/challenge-05-dashboard-resources.png create mode 100644 03-Azure/01-04-AI/08_Predictive_Maintenance/challenges/images/challenge-05-dashboard-traces.png create mode 100644 03-Azure/01-04-AI/08_Predictive_Maintenance/challenges/images/challenge-05-frontend-link.png create mode 100644 03-Azure/01-04-AI/08_Predictive_Maintenance/challenges/images/challenge-05-ports.png create mode 100644 03-Azure/01-04-AI/08_Predictive_Maintenance/challenges/images/challenge-05-scenario.png create mode 100644 03-Azure/01-04-AI/08_Predictive_Maintenance/challenges/images/challenge-05-workflow-architecture.png create mode 100644 03-Azure/01-04-AI/08_Predictive_Maintenance/challenges/images/dataexplorer-sample-query.png create mode 100644 03-Azure/01-04-AI/08_Predictive_Maintenance/challenges/images/hackathon-architecture.png create mode 100644 03-Azure/01-04-AI/08_Predictive_Maintenance/challenges/images/maintenance-scenario.png diff --git a/.devcontainer/03-azure-01-04-ai-08-predictive-maintenance/devcontainer.json b/.devcontainer/03-azure-01-04-ai-08-predictive-maintenance/devcontainer.json index 1c5ea4a8c..be6fad617 100644 --- a/.devcontainer/03-azure-01-04-ai-08-predictive-maintenance/devcontainer.json +++ b/.devcontainer/03-azure-01-04-ai-08-predictive-maintenance/devcontainer.json @@ -33,7 +33,7 @@ "workspaceFolder": "/workspaces/${localWorkspaceFolderBasename}/03-Azure/01-04-AI/08_Predictive_Maintenance", "postAttachCommand": "bash /workspaces/${localWorkspaceFolderBasename}/.devcontainer/welcome.sh", "remoteUser": "vscode", - // "postCreateCommand": "pip3 install --upgrade pip && pip3 install --user -r requirements.txt && bash /workspaces/${localWorkspaceFolderBasename}/.devcontainer/03-azure-01-04-ai-08-predictive-maintenance/setup-dotnet.sh", + "postCreateCommand": "uv sync --frozen && bash /workspaces/${localWorkspaceFolderBasename}/.devcontainer/03-azure-01-04-ai-08-predictive-maintenance/setup-dotnet.sh", "customizations": { "vscode": { "extensions": [ @@ -55,7 +55,10 @@ ], "settings": { "workbench.colorTheme": "GitHub Dark Default", - "git.openRepositoryInParentFolders": "always" + "git.openRepositoryInParentFolders": "always", + "workbench.editorAssociations": { + "*.md": "vscode.markdown.preview.editor" + } } } } diff --git a/.devcontainer/03-azure-01-04-ai-08-predictive-maintenance/setup-dotnet.sh b/.devcontainer/03-azure-01-04-ai-08-predictive-maintenance/setup-dotnet.sh new file mode 100644 index 000000000..9aab2b3e4 --- /dev/null +++ b/.devcontainer/03-azure-01-04-ai-08-predictive-maintenance/setup-dotnet.sh @@ -0,0 +1,12 @@ +#!/bin/bash +set -e + +echo "🔧 Setting up .NET environment for Challenge 3 and 5..." + +# Install Aspire +echo "📦 Installing .NET Aspire..." +curl -fsSL https://aspire.dev/install.sh | bash -s + +echo "✅ .NET environment setup complete!" +echo "📌 .NET SDK $(dotnet --version) is ready" +echo "📦 All required NuGet packages are cached and ready to use" diff --git a/03-Azure/01-04-AI/08_Predictive_Maintenance/.github/agents/agentplanning.agent.md b/03-Azure/01-04-AI/08_Predictive_Maintenance/.github/agents/agentplanning.agent.md new file mode 100644 index 000000000..edd214f53 --- /dev/null +++ b/03-Azure/01-04-AI/08_Predictive_Maintenance/.github/agents/agentplanning.agent.md @@ -0,0 +1,305 @@ +--- +description: 'Expert agent for planning and developing intelligent maintenance agents using .NET, Microsoft Foundry Agents SDK, and multi-agent patterns.' +tools: ['runCommands', 'runTasks', 'edit', 'search', 'new', 'runSubagent', 'usages', 'problems'] +--- + +You are an expert AI agent specializing in building intelligent maintenance and repair planning systems using .NET, Microsoft Foundry Agents SDK, and multi-agent architectures. You help developers create production-ready agents for industrial IoT and predictive maintenance scenarios. + +## Your Expertise + +### Core Competencies +- **Multi-Agent Systems**: Design and implementation of coordinated agent workflows (Anomaly Detection → Fault Diagnosis → Repair Planning → Scheduling) +- **.NET Development**: Modern C# patterns including primary constructors, async/await, dependency injection +- **Microsoft Foundry Agents SDK**: Creating and invoking Prompt Agents via `Azure.AI.Projects` and `Microsoft.Agents.AI` +- **Azure Cosmos DB**: NoSQL design patterns, efficient queries, partitioning strategies +- **Industrial IoT**: Predictive maintenance, threshold-based alerting, telemetry processing + +## Small-Model Robustness (CRITICAL) + +**IMPORTANT**: This agent may be used with smaller models (e.g., GPT-5-mini). Follow these constraints: + +1. **Produce complete files** - Generate 1-3 complete files per response, not partial snippets +2. **Keep it simple** - No extra abstractions, no extra models beyond what's specified +3. **Compile-first** - Always ensure code compiles before adding features +4. **Use pinned versions** - Always use the exact package versions listed below +5. **Use hardcoded mappings** - Implement fault→skills/parts as in-memory dictionaries (see mappings section) + +## Required NuGet Package Versions + +Always use these exact versions: + +```xml + + + + + + + + + + + +``` + +**Target Framework**: `net10.0` + +Add this to suppress preview API warnings: +```xml +$(NoWarn);CA2252 +``` + +## Environment Variables + +Use these exact names: +- `AZURE_AI_PROJECT_ENDPOINT` - Azure AI Foundry project endpoint +- `MODEL_DEPLOYMENT_NAME` - Model deployment name (e.g., "gpt-4o") +- `COSMOS_ENDPOINT` - Cosmos DB endpoint +- `COSMOS_KEY` - Cosmos DB key +- `COSMOS_DATABASE_NAME` - Cosmos DB database name + +## Foundry Agents SDK Pattern (REQUIRED) + +**CRITICAL**: Use the Foundry Agents SDK pattern, NOT direct ChatCompletions. This is the correct pattern: + +```csharp +using Azure.AI.Projects; +using Azure.AI.Projects.OpenAI; +using Microsoft.Agents.AI; + +// 1. Create AIProjectClient (uses DefaultAzureCredential) +var projectClient = new AIProjectClient(new Uri(endpoint), new DefaultAzureCredential()); + +// 2. Define and register the agent +var definition = new PromptAgentDefinition(model: modelDeploymentName) +{ + Instructions = "Your system prompt here..." +}; +await projectClient.Agents.CreateAgentVersionAsync("AgentName", new AgentVersionCreationOptions(definition)); + +// 3. Get and invoke the agent +var agent = projectClient.GetAIAgent(name: "AgentName"); +var response = await agent.RunAsync(userPrompt, thread: null, options: null); +string result = response.Text ?? ""; +``` + +## Project Structure + +Generate this exact structure: + +``` +RepairPlannerAgent/ +├── RepairPlannerAgent.csproj +├── Program.cs +├── RepairPlannerAgent.cs +├── Models/ +│ ├── DiagnosedFault.cs +│ ├── Technician.cs +│ ├── Part.cs +│ ├── WorkOrder.cs +│ ├── RepairTask.cs +│ └── WorkOrderPartUsage.cs +└── Services/ + ├── CosmosDbService.cs + ├── CosmosDbOptions.cs + └── FaultMappingService.cs +``` + +## Key Components + +### 1. RepairPlannerAgent.cs (Main Agent Class) + +Use primary constructor pattern: + +```csharp +public sealed class RepairPlannerAgent( + AIProjectClient projectClient, + CosmosDbService cosmosDb, + IFaultMappingService faultMapping, + string modelDeploymentName, + ILogger logger) +{ + private const string AgentName = "RepairPlannerAgent"; + private const string AgentInstructions = """ + You are a Repair Planner Agent for tire manufacturing equipment. + Generate a repair plan with tasks, timeline, and resource allocation. + Return the response as valid JSON matching the WorkOrder schema. + + Output JSON with these fields: + - workOrderNumber, machineId, title, description + - type: "corrective" | "preventive" | "emergency" + - priority: "critical" | "high" | "medium" | "low" + - status, assignedTo (technician id or null), notes + - estimatedDuration: integer (minutes, e.g. 60 not "60 minutes") + - partsUsed: [{ partId, partNumber, quantity }] + - tasks: [{ sequence, title, description, estimatedDurationMinutes (integer), requiredSkills, safetyNotes }] + + IMPORTANT: All duration fields must be integers representing minutes (e.g. 90), not strings. + + Rules: + - Assign the most qualified available technician + - Include only relevant parts; empty array if none needed + - Tasks must be ordered and actionable + """; + + public async Task EnsureAgentVersionAsync(CancellationToken ct = default) + { + var definition = new PromptAgentDefinition(model: modelDeploymentName) { Instructions = AgentInstructions }; + await projectClient.Agents.CreateAgentVersionAsync(AgentName, new AgentVersionCreationOptions(definition), ct); + } + + public async Task PlanAndCreateWorkOrderAsync(DiagnosedFault fault, CancellationToken ct = default) + { + // 1. Get required skills and parts from mapping + // 2. Query technicians and parts from Cosmos DB + // 3. Build prompt and invoke agent + // 4. Parse response and apply defaults + // 5. Save to Cosmos DB + } +} +``` + +### 2. FaultMappingService.cs (Hardcoded Mappings) + +Implement as in-memory dictionaries: + +```csharp +public interface IFaultMappingService +{ + IReadOnlyList GetRequiredSkills(string faultType); + IReadOnlyList GetRequiredParts(string faultType); +} + +public sealed class FaultMappingService : IFaultMappingService +{ + private static readonly IReadOnlyDictionary> FaultToSkills = + new Dictionary>(StringComparer.OrdinalIgnoreCase) + { + // Use mappings from section below + }; + + private static readonly IReadOnlyDictionary> FaultToParts = + new Dictionary>(StringComparer.OrdinalIgnoreCase) + { + // Use mappings from section below + }; +} +``` + +### 3. Models with Dual JSON Attributes + +Use both `System.Text.Json` and `Newtonsoft.Json` attributes for Cosmos DB compatibility: + +```csharp +using System.Text.Json.Serialization; +using Newtonsoft.Json; + +public sealed class WorkOrder +{ + [JsonPropertyName("id")] + [JsonProperty("id")] + public string Id { get; set; } = string.Empty; + + // ... other properties with both attributes +} +``` + +### 4. JSON Parsing with Number Handling + +LLMs sometimes return numbers as strings. Handle this: + +```csharp +private static readonly JsonSerializerOptions JsonOptions = new() +{ + PropertyNameCaseInsensitive = true, + NumberHandling = JsonNumberHandling.AllowReadingFromString, +}; +``` + +## Fault → Skills/Parts Mappings (CANONICAL SOURCE) + +Use these exact mappings in `FaultMappingService`: + +**Fault → Required Skills:** +- `curing_temperature_excessive` → `tire_curing_press`, `temperature_control`, `instrumentation`, `electrical_systems`, `plc_troubleshooting`, `mold_maintenance` +- `curing_cycle_time_deviation` → `tire_curing_press`, `plc_troubleshooting`, `mold_maintenance`, `bladder_replacement`, `hydraulic_systems`, `instrumentation` +- `building_drum_vibration` → `tire_building_machine`, `vibration_analysis`, `bearing_replacement`, `alignment`, `precision_alignment`, `drum_balancing`, `mechanical_systems` +- `ply_tension_excessive` → `tire_building_machine`, `tension_control`, `servo_systems`, `precision_alignment`, `sensor_alignment`, `plc_programming` +- `extruder_barrel_overheating` → `tire_extruder`, `temperature_control`, `rubber_processing`, `screw_maintenance`, `instrumentation`, `electrical_systems`, `motor_drives` +- `low_material_throughput` → `tire_extruder`, `rubber_processing`, `screw_maintenance`, `motor_drives`, `temperature_control` +- `high_radial_force_variation` → `tire_uniformity_machine`, `data_analysis`, `measurement_systems`, `tire_building_machine`, `tire_curing_press` +- `load_cell_drift` → `tire_uniformity_machine`, `load_cell_calibration`, `measurement_systems`, `sensor_alignment`, `instrumentation` +- `mixing_temperature_excessive` → `banbury_mixer`, `temperature_control`, `rubber_processing`, `instrumentation`, `electrical_systems`, `mechanical_systems` +- `excessive_mixer_vibration` → `banbury_mixer`, `vibration_analysis`, `bearing_replacement`, `alignment`, `mechanical_systems`, `preventive_maintenance` + +**Fault → Required Parts:** +- `curing_temperature_excessive` → `TCP-HTR-4KW`, `GEN-TS-K400` +- `curing_cycle_time_deviation` → `TCP-BLD-800`, `TCP-SEAL-200` +- `building_drum_vibration` → `TBM-BRG-6220` +- `ply_tension_excessive` → `TBM-LS-500N`, `TBM-SRV-5KW` +- `extruder_barrel_overheating` → `EXT-HTR-BAND`, `GEN-TS-K400` +- `low_material_throughput` → `EXT-SCR-250`, `EXT-DIE-TR` +- `high_radial_force_variation` → (empty array) +- `load_cell_drift` → `TUM-LC-2KN`, `TUM-ENC-5000` +- `mixing_temperature_excessive` → `BMX-TIP-500`, `GEN-TS-K400` +- `excessive_mixer_vibration` → `BMX-BRG-22320`, `BMX-SEAL-DP` + +**Default for unknown faults:** Return `["general_maintenance"]` for skills, empty array for parts. + +## Cosmos DB Structure + +Containers (partition keys): +- `Technicians` (partition key: `department`) +- `PartsInventory` (partition key: `category`) +- `WorkOrders` (partition key: `status`) + +## Code Style for Python Developers + +Add brief comments explaining C#-specific idioms: + +```csharp +// ??= means "assign if null" (like Python's: x = x or default_value) +wo.Priority ??= "medium"; + +// ?? means "if null, use this instead" (like Python's "or") +var name = technician.Name ?? "Unknown"; + +// Primary constructor - parameters become fields (like Python's __init__) +public sealed class MyClass(string name, ILogger logger) { } + +// await using - like Python's "async with" +await using var provider = services.BuildServiceProvider(); +``` + +## Response Pattern + +When generating code, always: + +1. **State what you'll create** - List the files +2. **Generate complete files** - No partial snippets +3. **Include all imports** - Don't assume implicit usings cover everything +4. **Show how to run** - Include the dotnet commands + +Example response structure: +``` +I'll create the following files: +1. Models/WorkOrder.cs - Work order data model +2. Services/FaultMappingService.cs - Skill/parts mappings + +[Complete file contents...] + +To build and run: +```bash +dotnet build +dotnet run +``` +``` + +## What NOT to Do + +- ❌ Don't use `Azure.AI.Inference` / `ChatCompletionsClient` - use Foundry Agents SDK +- ❌ Don't generate partial code snippets - generate complete files +- ❌ Don't add extra abstractions not in the spec +- ❌ Don't use different package versions than specified +- ❌ Don't skip error handling for JSON parsing +- ❌ Don't forget `NumberHandling.AllowReadingFromString` for LLM responses diff --git a/03-Azure/01-04-AI/08_Predictive_Maintenance/.github/copilot-instructions.md b/03-Azure/01-04-AI/08_Predictive_Maintenance/.github/copilot-instructions.md new file mode 100644 index 000000000..9dfcbf129 --- /dev/null +++ b/03-Azure/01-04-AI/08_Predictive_Maintenance/.github/copilot-instructions.md @@ -0,0 +1,72 @@ +# Copilot custom instructions (workshop mode) + +This repo is used for a workshop/exercise. + +## Key goal +Help the developer build the solution from context and instructions, **not** by copying an existing completed implementation. + +## Source of truth +- For Challenge 3 (Repair Planner), follow the agent guidance in `.github/agents/agentplanning.agent.md`. +- Use the **Foundry Agents SDK** pattern (`Azure.AI.Projects` + `Microsoft.Agents.AI`), not direct ChatCompletions. + +## Do not use solution code as a template +Unless the user explicitly asks to reveal or compare with a solution, **do not open, reference, or reuse code** from any folder intended to contain completed answers, for example: +- `walkthrough/**/RepairPlanner/**` +- `walkthrough/**` +- `**/*solution*/**` +- `**/*Solution*/**` +- `**/reference/**` + +If such folders exist in the workspace, treat them as off-limits. + +## How to proceed instead +- Ask clarifying questions only when required. +- Implement incrementally in the exercise project. +- Prefer small, verifiable changes; keep logging helpful. + +## Smaller-model friendly (CRITICAL) + +This workshop must work reliably with smaller models (e.g., GPT-5-mini). Follow these constraints: + +### Output constraints +- Prefer producing a small number of **complete files** per request (1-3) over partial snippets +- Keep responses structured: **What you will change**, **Files**, **How to run/verify** +- Avoid optional extras unless explicitly requested (no extra abstractions, no extra models) +- When uncertain, state assumptions explicitly and proceed with a safe default + +### Pinned versions (Challenge 3) +Always use these exact NuGet package versions: +- `Azure.AI.Projects` → `2.1.0-beta.4` +- `Azure.Identity` → `1.21.0` +- `Microsoft.Agents.AI` → `1.20.0` +- `Microsoft.Agents.AI.Foundry` → `1.20.0-preview.260831.1` +- `Microsoft.Extensions.AI` → `10.9.0` +- `Microsoft.Extensions.AI.Abstractions` → `10.9.0` +- `Microsoft.Azure.Cosmos` → `3.63.0` +- `Microsoft.Extensions.DependencyInjection` → `10.0.12` +- `Microsoft.Extensions.Logging` → `10.0.12` +- `Microsoft.Extensions.Logging.Console` → `10.0.12` +- `Newtonsoft.Json` → `13.0.4` + +### Environment variables (Challenge 3) +- `AZURE_AI_PROJECT_ENDPOINT` +- `MODEL_DEPLOYMENT_NAME` +- `COSMOS_ENDPOINT` +- `COSMOS_KEY` +- `COSMOS_DATABASE_NAME` + +### Fault→skills/parts mappings +Use the hardcoded mappings from `.github/agents/agentplanning.agent.md` - implement as in-memory dictionaries in `FaultMappingService`. + +## Code style for Python developers + +Add brief comments explaining C#-specific idioms: +- `??` and `??=` operators (null coalescing) +- Primary constructors +- `await using` (like Python's `async with`) + +## What NOT to do +- ❌ Don't use `Azure.AI.Inference` / `ChatCompletionsClient` - use Foundry Agents SDK +- ❌ Don't generate partial code snippets - generate complete files +- ❌ Don't add extra abstractions not in the spec +- ❌ Don't use different package versions than specified diff --git a/03-Azure/01-04-AI/08_Predictive_Maintenance/README.md b/03-Azure/01-04-AI/08_Predictive_Maintenance/README.md index 1333ed77b..04bfcbad1 100644 --- a/03-Azure/01-04-AI/08_Predictive_Maintenance/README.md +++ b/03-Azure/01-04-AI/08_Predictive_Maintenance/README.md @@ -1 +1,308 @@ -TODO +# Agentic AI Hacks | Predictive Maintenance + +## Introduction + +Manufacturing teams need to turn raw machine telemetry into fast, consistent +maintenance decisions. Manual triage is slow, root-cause analysis depends on +scattered historical knowledge, and coordinating parts, technicians, and +schedules across systems is hard to keep consistent. + +This microhack builds an **agentic predictive maintenance workflow** for a +fictitious tire manufacturing company. You will implement five focused agents +that mirror a real maintenance process — from detecting an anomaly to +diagnosing its cause, planning the repair, scheduling a technician, and +ordering parts — using **Microsoft Agent Framework**, **Microsoft Foundry**, +and **Azure**. + +During this microhack, you will build individual agent applications (mostly in +Python, with one in .NET), connect them to shared factory data, and finally +orchestrate them into a single observable workflow hosted in **.NET Aspire**. + +--- + +## Scenario + +This microhack uses a fictitious tire manufacturing company as an example, but +the scenario is applicable to most manufacturing environments where machines +are involved in production. + +The image below illustrates the conceptual scenario. + +![Manufacturing scenario](./challenges/images/maintenance-scenario.png) + +❶ The factory operates a production line made up of multiple specialized +machines, each responsible for a distinct step in tire manufacturing (mixing, +extrusion, curing, inspection). + +❷ A key business metric is **Overall Equipment Effectiveness (OEE)** — how +effectively equipment is utilized during planned production time. Unplanned +downtime and quality losses directly reduce throughput and profitability. + +❸ Maintenance technicians perform both *planned* (time/usage-based) +maintenance and *condition-based* maintenance triggered by sensor readings and +detected anomalies. Because machines are complex, tasks require specific +skills, certifications, and safety procedures. + +❹ Spare parts are held in a local warehouse with limited stock. When +inventory falls below thresholds or parts are needed for a repair, +replenishment is coordinated with external suppliers, often with lead times +and ordering constraints. + +❺ The production environment integrates with multiple core systems (WMS, ERP, +CMMS, MES, QMS, knowledge bases). These systems coordinate production, +maintenance execution, inventory, quality, and scheduling — making end-to-end +orchestration essential. + +The maintenance process is complex and requires coordination across people, +parts, and systems. Common challenges include: + +* Detecting when a machine needs attention, who should be notified, and how + to prioritize the response. +* Diagnosing the root cause of irregular machine behavior using telemetry + plus shared knowledge and historical patterns. +* Determining which parts are required, whether they are in local stock, and + how to handle supplier lead times. +* Matching work to technicians with the right skills, certifications, and + safety requirements. +* Scheduling technicians based on availability, shifts, and constraints + (access windows, downtime limits). +* Finding the optimal maintenance window that minimizes production + disruption while keeping risk acceptable. + +Agents can help by coordinating data, decisions, and handoffs across these +steps. The intent is not to replace technicians or planners, but to reduce +time-to-triage and make the workflow more consistent and observable. + +The image below shows the agent roles used in this microhack and how they +interact with telemetry and existing factory systems. + +![Agentic factory orchestration](./challenges/images/agentic-maintenance-orchestration.png) + +❶ Telemetry is emitted from machine sensors and evaluated for deviations +(simple thresholds or an anomaly detection model). + +❷ The **Anomaly Classification Agent** classifies the situation +(normal/warning/critical), enriches it with machine context, and raises an +alert with an appropriate priority. + +❸ The **Fault Diagnosis Agent** proposes likely root causes using telemetry +plus documented historical knowledge (runbooks, prior work orders, technician +notes). + +❹ The **Repair Planner Agent** translates a diagnosis into an actionable +repair plan — required parts, estimated effort, and required +skills/certifications — and drafts a work order for review. + +❺ The **Maintenance Scheduler Agent** proposes a maintenance window based on +priority/risk, technician availability, and production constraints. + +❻ The **Parts Ordering Agent** checks inventory and suggests replenishment +orders based on supplier lead times and constraints. + +The factory process spans many systems, teams, and areas of responsibility. +This microhack embraces that reality: agents stay independent, but are +designed to collaborate through well-defined inputs/outputs and shared +observability. + +--- + +## Architecture + +You will build multiple agent applications (Python and .NET) and connect them +using a sequential workflow in the **Microsoft Agent Framework**. The workflow +itself is described in the scenario section above; the image below shows the +supporting platform components. + +![Factory Hackathon Architecture](./challenges/images/hackathon-architecture.png) + +❶ The documentation, starter code, and sample assets are available in this +repository, opened directly from your assigned lab account. + +❷ **GitHub Codespaces** provides a containerized development environment with +the required tools and extensions pre-installed, so everyone starts from a +known baseline. + +❸ You will build individual agent applications (mostly in Python, with one in +.NET) and eventually run the end-to-end workflow locally using **Aspire**, +which also gives you a single place to start the system and inspect service +logs. + +❹ **GitHub Copilot** is used to co-develop parts of the solution — one +challenge uses it to build an agent from scratch — and you can use it +throughout the microhack for implementation, refactoring, and troubleshooting. + +❺ Agents are registered in **Microsoft Foundry** and configured with the +selected model deployments plus the tools and data connections they need. + +❻ **API Management** acts as an AI gateway for selected endpoints. When +needed, agents access those APIs via **MCP**, which provides a consistent +interface for tool calling. + +❼ **Foundry IQ / knowledge sources** provide content for retrieval-augmented +generation (RAG), such as machine documentation, troubleshooting guides, and +historical notes. + +❽ Factory data (machines, thresholds, inventory, work orders) is stored in +**Cosmos DB** and **Blob Storage** and used by agents during analysis and +planning. + +❾ **Application Insights** (and related logs/traces) provides observability +across the workflow so you can troubleshoot agent behavior, tool calls, and +end-to-end requests. + +> [!NOTE] +> Manufacturing environments are complex: legacy systems, safety +> requirements, strict uptime targets, and processes that exist for good +> reasons — often split across distributed teams and both IT and OT domains. +> +> This microhack uses a deliberately simplified lab setup so you can focus on +> the learning objectives and the core patterns of agentic development +> (workflow design, tool integration, and observability), rather than the +> full complexity of factory networking, governance, and infrastructure. + +### The five agents + +| Agent | Role | Data source or control | Introduced in | +|---------------------------------|-----------------------------------------------------------------------|------------------------------------------------|---------------| +| Anomaly Classification Agent | Classifies telemetry deviations and raises prioritized alerts | Machine thresholds and telemetry | Challenge 2 | +| Fault Diagnosis Agent | Proposes likely root causes | Knowledge base and maintenance history | Challenge 2 | +| Repair Planner Agent | Drafts a repair plan and work order | Parts inventory and technician skills | Challenge 3 | +| Maintenance Scheduler Agent | Proposes a maintenance window | Technician availability and production windows | Challenge 4 | +| Parts Ordering Agent | Checks inventory and suggests replenishment | Parts inventory and suppliers | Challenge 4 | + +--- + +## Learning objectives + +By participating in this microhack, you will learn how to: + +* Implement five focused agents and connect them to data and tools +* Use GitHub Copilot, including a custom `agentplanning` coding agent, to + accelerate agent development while keeping changes reviewable +* Add persistent agent memory (threads) for maintenance scenarios where + history matters +* Instrument agents so you can inspect outputs, tool calls, logs, and traces +* Build an end-to-end agent workflow using the Microsoft Agent Framework and + sequential orchestration hosted in **Aspire**, with agent-to-agent (A2A) + communication + +--- + +## Requirements + +To complete the microhack, you will need: + +* A [GitHub account](https://github.com/signup) and access to the repository +* Familiarity with Python or .NET, including handling JSON data and making API calls +* Familiarity with generative AI solutions and Azure services +* Python 3.11+ and [uv](https://docs.astral.sh/uv/), plus .NET 10 (provided in + the devcontainer) +* Azure resources and environment values supplied by the MicroHack platform or + your event coach + +### Participant setup + +Complete all environment preparation in +[Challenge 1: Environment Setup and Data Foundation](./challenges/challenge-01.md). +It covers the Codespaces setup, dependency installation, Azure authentication, +and validation required before beginning the build challenges. + +--- + +## Repository structure + +```text +08_Predictive_Maintenance/ +|-- challenges/ # Challenge instructions (challenge-01 through challenge-05) +|-- data/ # Sample factory data (machines, thresholds, work orders, knowledge base) +|-- src/ # Starter and required agent code, and the end-to-end workflow app +|-- labautomation/ # MicroHack provisioning and Azure deployment template +|-- walkthrough/ # Reference solutions and optional supplementary material per challenge +|-- .github/agents/ # Custom GitHub Copilot agent used in Challenge 3 +|-- pyproject.toml # Pinned participant Python environment +`-- README.md +``` + +| Path | Who uses it | Purpose | +|-----------------------------------------|-----------------------------|----------------------------------------------------------------| +| [`challenges/`](./challenges/) | Attendees | Step-by-step instructions for the five challenges | +| [`data/`](./data/) | Attendees and scripts | Sample factory data used by the agents | +| [`src/`](./src/) | Attendees | Required starter agents and the end-to-end workflow application | +| [`labautomation/`](./labautomation/) | Platform and coaches | Per-lab provisioning and Azure Bicep deployment template | +| [`walkthrough/`](./walkthrough/) | Facilitators and attendees | Reference solutions and optional/supplementary material per challenge | +| [`.github/agents/`](./.github/agents/) | Attendees | Custom Copilot agent (`agentplanning`) used in Challenge 3 | + +--- + +## Challenges + +This microhack is divided into five challenges, each building on the previous +one. You'll start by setting up your environment, then progressively build +individual agents, and finally orchestrate them into a complete workflow. + +### Challenge structure + +Each challenge follows a consistent structure: + +| Section | Description | +|---------|-------------| +| 🎯 **Objective** | Learning goals for the challenge | +| 🧭 **Context and Background** | Scenario explanation, architecture diagrams, and technical concepts | +| ✅ **Tasks** | Step-by-step instructions to complete the challenge | +| 🚀 **Go Further** | Optional stretch goals if you finish early | +| 🛠️ **Troubleshooting** | Common problems and solutions | +| 🧠 **Conclusion and Reflection** | Recap, key takeaways, and further reading | + +Each challenge also includes a **"Stuck? See the walkthrough"** callout that +links to the matching `walkthrough/challenge-XX/solution-XX.md`, which adds +prerequisites, exact commands, expected results, troubleshooting, and a +validation checklist without repeating the challenge narrative. + +### Challenge list + +| # | Challenge | Description | Duration | +|---|-----------|-------------|----------| +| 1 | [Environment Setup and Data Foundation](./challenges/challenge-01.md) | Deploy Azure resources, configure environment variables, and seed sample factory data | 30 min | +| 2 | [Anomaly Detection and Fault Diagnosis Agents](./challenges/challenge-02.md) | Build an Anomaly Classification Agent and a Fault Diagnosis Agent for tire manufacturing telemetry | 60 min | +| 3 | [Repair Planner Agent and AI-Driven Development](./challenges/challenge-03.md) | Use the custom `agentplanning` Copilot agent to build a Repair Planner Agent in .NET | 60 min | +| 4 | [Maintenance Scheduler and Parts Ordering Agents](./challenges/challenge-04.md) | Build Scheduler and Parts Ordering agents with persistent memory and observability | 60 min | +| 5 | [Multi-Agent Orchestration](./challenges/challenge-05.md) | Compose the five agents into a workflow with Microsoft Agent Framework, hosted in Aspire | 60 min | + +> [!TIP] +> While it's possible to rush through the challenges, we encourage you to pause +> and reflect along the way. Consider how each pattern relates to your own +> context: what business processes in your environment could benefit from +> coordinated AI agents? + +### Solution walkthroughs + +* [Challenge 1 walkthrough](./walkthrough/challenge-01/solution-01.md) +* [Challenge 2 walkthrough](./walkthrough/challenge-02/solution-02.md) +* [Challenge 3 walkthrough](./walkthrough/challenge-03/solution-03.md) +* [Challenge 4 walkthrough](./walkthrough/challenge-04/solution-04.md) +* [Challenge 5 walkthrough](./walkthrough/challenge-05/solution-05.md) + +--- + +## Contributing + +This project welcomes contributions and suggestions. Most contributions require you to +agree to a Contributor License Agreement (CLA) declaring that you have the right to, +and do, grant us the rights to use your contribution. For details, visit the +[Microsoft CLA website](https://cla.opensource.microsoft.com/). + +This project has adopted the +[Microsoft Open Source Code of Conduct](https://opensource.microsoft.com/codeofconduct/). +For more information, see the +[Code of Conduct FAQ](https://opensource.microsoft.com/codeofconduct/faq/) or contact +[opencode@microsoft.com](mailto:opencode@microsoft.com). + +## Trademarks + +This project may contain trademarks or logos for projects, products, or services. +Authorized use of Microsoft trademarks or logos is subject to the +[Microsoft Trademark and Brand Guidelines](https://www.microsoft.com/legal/intellectualproperty/trademarks/usage/general). +Use of Microsoft trademarks or logos in modified versions of this project must not +cause confusion or imply Microsoft sponsorship. Any use of third-party trademarks or +logos is subject to those third parties' policies. + diff --git a/03-Azure/01-04-AI/08_Predictive_Maintenance/challenges/challenge-01.md b/03-Azure/01-04-AI/08_Predictive_Maintenance/challenges/challenge-01.md new file mode 100644 index 000000000..2762d2424 --- /dev/null +++ b/03-Azure/01-04-AI/08_Predictive_Maintenance/challenges/challenge-01.md @@ -0,0 +1,165 @@ +# Challenge 1 - Environment Setup and Data Foundation + +**[Home](../README.md)** | [Next challenge](challenge-02.md) + +> [!TIP] +> Stuck on a step? See the [Challenge 1 walkthrough](../walkthrough/challenge-01/solution-01.md) for exact commands, expected output, and a validation checklist. + +## 🎯 Objective + +Sign in with your assigned lab credentials, verify the pre-provisioned Azure resources and seeded factory data, and start the GitHub Codespaces development environment used throughout the rest of this microhack. + +## 🧭 Context and Background + +Before you can build any agents, the tire factory's data foundation and Azure resources need to be in place. The MicroHack platform provisions and seeds these ahead of time so you can focus on building agents rather than infrastructure. All resources for your lab live in a single Azure resource group, as illustrated below. + +Azure resources used in this microhack + +* A **Foundry Account** and **Foundry Project** with model deployments for `gpt-5.4`, `gpt-5.4-mini`, and `text-embedding-3-large` +* **API Management** with API proxies that read data from Cosmos DB +* **Cosmos DB** with containers for machines, thresholds, telemetry, the knowledge base, parts inventory, technicians, and work orders +* A **Storage account** with knowledge base wiki articles in Markdown +* **Azure AI Search** to query the wiki knowledge base +* **Application Insights** and **Log Analytics** for observability +* **Container Registry** and a **Container Apps Environment** for the agents you deploy in later challenges + +
+Cosmos DB data model (7 containers) + +| Container | Partition key | Purpose | Sample count | +|-----------|--------------|---------|--------------| +| `Machines` | `/type` | Equipment definitions | 5 machines | +| `Thresholds` | `/machineType` | Operating limits | 13 thresholds | +| `Telemetry` | `/machineId` | Sensor readings | 10 samples | +| `KnowledgeBase` | `/machineType` | Troubleshooting articles | 10 articles | +| `PartsInventory` | `/category` | Spare parts | 16 parts | +| `Technicians` | `/department` | Maintenance staff | 6 technicians | +| `WorkOrders` | `/status` | Maintenance history | 5 work orders | + +
+ +
+Seeded telemetry anomalies you will investigate in later challenges + +* 🔴 **Machine 001** (Tire Curing Press A1): Temperature 179.2°C — exceeds the 178°C warning threshold +* 🔴 **Machine 002** (Tire Building Machine B1): Drum vibration 3.2 mm/s — exceeds 3.0 mm/s +* 🔴 **Machine 003** (Tire Extruder C1): Throughput 640 kg/h — below the 650 kg/h minimum +* 🔴 **Machine 004** (Tire Uniformity Machine D1): Radial force variation 105 N — exceeds 100 N +* 🔴 **Machine 005** (Banbury Mixer E1): Multiple warnings across temperature, power, and vibration + +
+ +The remaining challenges use this pre-provisioned environment and a repository hosted on GitHub. You will work from a GitHub Codespace configured with the Python, .NET, and Azure tooling required to build the predictive maintenance agents. + +Complete this setup with the credentials provided in your Hackbox. Use the assigned lab account rather than a personal or work account so that you have access to the correct Azure subscription, resource group, GitHub organization, and repository. + +## ✅ Tasks + +### 1. Sign in to Azure + +Open the [Azure portal](https://portal.azure.com) and sign in with the credentials provided in your Hackbox, choosing **Use another account** when prompted. + +In the Azure portal, select **Resource groups**, open the resource group assigned to you, and confirm that its resources have been deployed successfully. Verify that you can see the Foundry project, the Cosmos DB account, the Storage account, and the API Management instance described in [Context and Background](#-context-and-background). + +### 2. Sign in to GitHub + +Open [GitHub](https://github.com) and sign in with the credentials provided in your Hackbox, choosing **Use another account** and then **Sign in with your identity provider**. + +Open the organization associated with your lab account and select the `MicroHack` repository. Confirm that you can see the repository files and folders, including `03-Azure/01-04-AI/08_Predictive_Maintenance`. + +### 3. Create the development environment + +From the repository page, select **Code**, open the **Codespaces** tab, then use the `...` menu to choose **New with options**. For **Dev container configuration**, select **Azure / AI / Predictive Maintenance**, then select **Create codespace**. + +GitHub opens the Codespace in a new browser tab. Wait for the container setup to finish, then confirm that the repository files are visible in the Explorer and that the integrated terminal opens without errors. + +Open a terminal in the Codespace and verify that the required tools are installed: + +```bash +az --version +dotnet --version +``` + +Install the pinned Python dependencies and activate the environment: + +```bash +uv sync --frozen +source .venv/bin/activate +``` + +Sign in to Azure from the Codespace terminal with the assigned lab account: + +```bash +az login --use-device-code +``` + +### 4. Retrieve environment configuration + +From the repository root, run the setup script with the resource group name provided in your Hackbox: + +```bash +bash labautomation/get-keys.sh --resource-group "" +``` + +The script locates your deployed lab resources and creates a local `.env` file. It retrieves the Foundry project endpoint, Cosmos DB connection details, Storage account connection string, and Azure AI Search key and endpoint required by the remaining challenges. The file is excluded from Git and its secret values are not displayed. + +Make the environment variables available in your shell: + +```bash +export $(grep -v '^#' .env | xargs) +``` + +> [!TIP] +> Re-export the environment variables each time you open a new terminal or resume a stopped Codespace. + +### 5. Verify the seeded factory data + +The lab deployment seeds the Cosmos DB containers and uploads the knowledge base wiki articles automatically — you do not need to run a seeding step yourself. Confirm the data is present: + +```bash +az cosmosdb sql container list \ + --account-name "$COSMOS_ACCOUNT_NAME" \ + --resource-group "$RESOURCE_GROUP" \ + --database-name "FactoryOpsDB" \ + --query "[].id" --output tsv +``` + +You should see all seven containers listed in [Context and Background](#-context-and-background). Then confirm the knowledge base wiki articles were uploaded to Blob Storage: + +```bash +az storage blob list \ + --connection-string "$AZURE_STORAGE_CONNECTION_STRING" \ + --container-name kb-wiki \ + --query "length(@)" --output tsv +``` + +The expected result is `5` wiki articles, one per machine type. + +## 🚀 Go Further + +Review the [solution architecture](../README.md#architecture) and identify which Azure resource each of the five agents will use in the upcoming challenges. + +## 🛠️ Troubleshooting + +* **The Azure resource group is missing:** Confirm that you signed in with the assigned lab account and selected the assigned subscription in the Azure portal. +* **`get-keys.sh` reports an authorization error:** Confirm that the Codespace is signed in with the assigned lab account, and that it can read deployment outputs and list keys for the Foundry, Cosmos DB, Storage, and Azure AI Search resources. +* **A Cosmos DB container is missing or empty, or the `kb-wiki` container has fewer than 5 blobs:** See the [Challenge 1 walkthrough](../walkthrough/challenge-01/solution-01.md) for the fallback re-seed command. +* **The GitHub organization or repository is missing:** Sign out of GitHub, then sign in again through **Sign in with your identity provider** using the Hackbox credentials. +* **The Codespace does not finish starting:** Review the creation log for the failing step, then rebuild the container or recreate the Codespace. + +## 🧠 Conclusion and Reflection + +You have verified the Azure resources, confirmed the seeded factory data, and opened the configured development environment. This forms the foundation for the multi-agent predictive maintenance system you will build over the next four challenges. + +> [!IMPORTANT] +> This microhack uses simplified, key-based authentication and public network access for learning purposes. Production systems should implement managed identities, private endpoints, Azure Key Vault for secrets, and RBAC for fine-grained access control. + +If you want to learn more about what you covered in this challenge, check out the links below: + +* [Get started with Azure CLI](https://learn.microsoft.com/cli/azure/get-started-with-azure-cli) +* [Azure Cosmos DB documentation](https://learn.microsoft.com/azure/cosmos-db/) +* [Microsoft Foundry documentation](https://learn.microsoft.com/azure/ai-foundry/) +* [Azure AI Search documentation](https://learn.microsoft.com/azure/search/) +* [Predictive maintenance patterns](https://learn.microsoft.com/azure/architecture/data-guide/scenarios/predictive-maintenance) + +**Next:** [Challenge 2 - Anomaly Detection and Fault Diagnosis Agents](challenge-02.md) diff --git a/03-Azure/01-04-AI/08_Predictive_Maintenance/challenges/challenge-02.md b/03-Azure/01-04-AI/08_Predictive_Maintenance/challenges/challenge-02.md new file mode 100644 index 000000000..f2c226a87 --- /dev/null +++ b/03-Azure/01-04-AI/08_Predictive_Maintenance/challenges/challenge-02.md @@ -0,0 +1,384 @@ +# Challenge 2 - Anomaly Detection and Fault Diagnosis Agents + +**[Home](../README.md)** | [Previous challenge](challenge-01.md) | [Next challenge](challenge-03.md) + +> [!TIP] +> Stuck on a step? See the [Challenge 2 walkthrough](../walkthrough/challenge-02/solution-02.md) for exact commands, expected output, and a validation checklist. + +## 🎯 Objective + +The goals for this challenge are: + +* Create two Foundry Agents in Python: an **Anomaly Classification Agent** and a **Fault Diagnosis Agent** +* Use MCP servers exposed through API Management for remote tool invocation +* Ground the Fault Diagnosis Agent with your own data using **Foundry IQ** + +## 🧭 Context and Background + +![Challenge 2 scenario](./images/challenge-02-scenario.png) + +The **Anomaly Classification Agent** interprets detected anomalies and raises corresponding maintenance alerts. The **Fault Diagnosis Agent** determines the root cause of an anomaly so a technician can prepare for maintenance. Both agents use a number of different tools to accomplish their tasks. + +You will use the Azure resources highlighted in the image below. + +Azure Resources + +### Model Context Protocol (MCP) + +You will use **Model Context Protocol (MCP)** to connect the agents with remote tools. MCP is a standard way for an agent to discover and invoke external capabilities ("tools") through a consistent interface. + +MCP + +Let's break down the key components shown in the diagram: + +#### ❶ MCP Host and MCP Client + +An **MCP Host** is the application that runs agents and manages MCP connections. Inside the host lives one or more **MCP Clients** that handle communication with MCP servers. Examples include: + +| MCP Host | MCP Client | Description | +|----------|------------|-------------| +| VS Code | GitHub Copilot extension | Copilot acts as the MCP client, connecting to servers for code tools | +| Your Python script | Agent SDK client | The agents you build in this challenge — the `MCPTool` object creates an MCP client | + +#### ❷ Remote MCP Server (HTTPS) + +A **remote MCP server** runs in the cloud and communicates over HTTPS. This is ideal for enterprise APIs that need to be accessed securely, shared services used by multiple agents, and APIs that require authentication and rate limiting. In this challenge, you expose APIs via **API Management** as remote MCP servers. + +#### ❸ Tools and Data + +MCP servers provide access to **tools** (actions the agent can invoke) and **data** (information the agent can retrieve). The server defines tool names and descriptions, input/output schemas (JSON Schema), and the actual implementation that fetches data or performs actions. + +#### ❹ Local MCP Server (stdio) + +A **local MCP server** runs on the same machine as the host and communicates via standard input/output (stdio). Useful for development and testing, tools that need local file system access, and quick prototyping without cloud deployment. + +#### ❺ MCP Server Internals + +Whether remote or local, every MCP server implements the same protocol: + +| Component | Purpose | +|-----------|---------| +| Discovery API | `GET /mcp` — returns available tools with their schemas | +| Invocation API | `POST /mcp/tools/{tool}` — executes a specific tool | +| SDKs | Libraries in Python, TypeScript, and C# to simplify building servers | + +While you *could* build an MCP server yourself using an SDK, there are easier ways of doing it *without writing any code*. The next section shows how API Management lets you expose existing APIs as MCP servers. + +### API Management as AI gateway + +In this challenge, the tools live behind remote MCP servers hosted in **API Management** that expose existing API operations. Why use API Management here: + +* **Decouples the agent from integrations**: the agent calls a tool by name and schema, not by hardcoding HTTP requests. +* **Reusable and portable**: the same agent code can work across environments as long as the MCP server URL/connection is configured. +* **Governance hooks**: tools can be allow-listed and can require (or skip) approval depending on your scenario. + +The diagram below illustrates how API Management serves as an AI gateway, bridging traditional API patterns with the new requirements of agentic applications. + +AI Gateway + +| # | Component | Description | +|---|-----------|-------------| +| ❶ | Traditional Application | APIs have been used for many years to integrate applications with remote systems. This is the foundation we build upon. | +| ❷ | API Proxy (APIM) | API Management is a mature platform that shields backend systems and adds quality-of-service capabilities such as throttling, security, and caching. | +| ❸ | Traditional Backend APIs | Backend services exposed via REST, SOAP, GraphQL, and other protocols that your applications consume. | +| ❹ | Agentic Application | We use APIs just like traditional applications to build the full solution. However, agentic applications introduce new requirements around AI model integration and tool discovery. | +| ❺ | AI APIs | Model APIs where APIM policies enable token limiting, token metrics emission, model load balancing, and content safety — all using built-in APIM functionality applied in new ways for AI workloads. | +| ❻ | MCP in APIM | A standard API can easily be exposed in APIM as an MCP server without writing any code. This is exactly what you will do in this challenge — turning existing APIs into tools that agents can discover and invoke. | + +### Grounding with Foundry IQ + +You will also ground the Fault Diagnosis Agent with data using **Foundry IQ**, a retrieval-augmented generation (RAG) capability in Microsoft Foundry that enables agents to access and reason over your enterprise data. It provides a managed knowledge retrieval layer powered by Azure AI Search, letting your agent retrieve relevant chunks from your own documents at runtime instead of pasting wiki content into prompts. + +Key benefits of Foundry IQ: + +* **Simplified development**: no need to build custom RAG pipelines — Foundry IQ handles chunking, indexing, and retrieval for you. +* **Flexible data integration**: connect multiple data sources to a single knowledge base. +* **Enhanced agent capabilities**: agents can access up-to-date enterprise knowledge without retraining the model. + +Foundry IQ consists of two main concepts: + +| Concept | Description | +|---------|-------------| +| Knowledge Sources | The *what* to retrieve — connections to your data. These define where the content comes from. | +| Knowledge Bases | The *how* to retrieve — the retrieval configuration that combines one or more knowledge sources into a queryable endpoint. | + +Foundry IQ Architecture + +| # | Component | Description | +|---|-----------|-------------| +| ❶ | End User | The agent receives questions from the end user and processes them. | +| ❷ | Large Language Model | The agent uses a large language model to reason about the question and determine the next action. | +| ❸ | Knowledge Base | Foundry IQ provides a knowledge base with several knowledge sources that the agent can query. | +| ❹ | Indexed Sources | Pre-indexed sources like Azure AI Search indexes, Azure Blob Storage, and Azure Data Lake. Content is indexed ahead of time for fast vector and keyword retrieval. | +| ❺ | Remote Sources | On-demand sources like SharePoint and Bing that are queried at runtime. Useful for real-time data that changes frequently. | + +In this challenge, you will use Foundry IQ to make the machine wiki available to the Fault Diagnosis Agent via an MCP tool. + +## ✅ Tasks + +### Task 1: Create and test the initial Anomaly Classification Agent + +As a first step, create an agent to interpret and classify anomalies and raise maintenance alerts if certain thresholds have been violated. The agent takes anomalies for specific machines as input and checks them against thresholds for that machine type by using JSON data stored in Cosmos DB. + +#### Task 1.1: Review the initial code + +Examine the Python code in [`src/agents/anomaly_classification_agent.py`](../src/agents/anomaly_classification_agent.py). A few things to observe: + +* The agent uses two function tools: `get_thresholds` retrieves specific metric threshold values for certain machine types, and `get_machine_data` fetches details about machines such as id, model, and maintenance history. +* The agent is instructed to output both structured alert data in a specific format and a human-readable summary. +* The code creates the agent and runs a sample query against it. + +#### Task 1.2: Run the code + +```bash +python src/agents/anomaly_classification_agent.py +``` + +Verify that the agent responds with a reasonable answer that classifies the sample anomaly and includes a human-readable summary. + +### Task 2: Equip the agent with MCP tools + +Machine and threshold information is typically stored in a central system and exposed through an API. Adjust the data access to use existing Machine and Maintenance APIs instead of accessing Cosmos DB directly, by exposing those APIs as MCP servers. + +#### Task 2.1: Test the Machine API + +The Machine API and Maintenance API are already available in API Management and contain endpoints for getting details about a specific machine and thresholds for certain machine types. Try the APIs: + +```bash +# Get all machines +curl -fsSL "$APIM_GATEWAY_URL/machine" -H "Ocp-Apim-Subscription-Key: $APIM_SUBSCRIPTION_KEY" -H "Accept: application/json" + +# Get a specific machine +curl -fsSL "$APIM_GATEWAY_URL/machine/machine-001" -H "Ocp-Apim-Subscription-Key: $APIM_SUBSCRIPTION_KEY" -H "Accept: application/json" + +# Get thresholds for a machine type +curl -fsSL "$APIM_GATEWAY_URL/maintenance/tire_curing_press" -H "Ocp-Apim-Subscription-Key: $APIM_SUBSCRIPTION_KEY" -H "Accept: application/json" +``` + +#### Task 2.2: Expose APIs as MCP servers + +API Management provides an easy way to expose APIs as MCP servers without writing any additional wrapper code. + +❶ Navigate to your API Management instance in the [Azure portal](https://portal.azure.com). + +❷ Choose **APIs** and notice that the *Machine API* and *Maintenance API* you tested earlier are available. + +Portal APIs + +❸ Navigate to the **MCP Servers** section. + +❹ Select **Create MCP Server** and **Expose an API as MCP Server**. + +❺ Select the API, operations, and provide the following details: + +* **API**: *Machine API* +* **API Operations**: *Get Machine* +* **Display Name**: *Get Machine Data* +* **Name**: `get-machine-data` +* **Description**: *Gets details about a specific machine* + +Expose MCP + +❻ Select **Create**. + +❼ Copy the *MCP Server URL* of the newly created MCP server. + +MCP URL + +❽ Add a new entry to the `.env` file: `MACHINE_MCP_SERVER_ENDPOINT=""`. + +Repeat the same steps to create the *Maintenance* MCP server using the following settings: + +* **API**: *Maintenance API* +* **API Operations**: *Get Threshold* +* **Display Name**: *Get Maintenance Data* +* **Name**: `get-maintenance-data` +* **Description**: *Gets maintenance data such as thresholds for maintenance alerts* + +Save the MCP Server URL as `MAINTENANCE_MCP_SERVER_ENDPOINT=""`, then reload the environment variables: + +```bash +export $(grep -v '^#' .env | xargs) +``` + +#### Task 2.3: Use the MCP servers from the agent + +Replace the direct database access with the new Machine and Maintenance MCP servers, added as tools to the Anomaly Classification Agent. + +Examine the Python code in [`src/agents/anomaly_classification_agent_mcp.py`](../src/agents/anomaly_classification_agent_mcp.py). A few things to observe: + +* The agent uses two MCP tools: `machine-data` fetches details about machines, and `maintenance-data` retrieves specific metric threshold values. +* A project connection is created for each MCP tool. + +#### Task 2.4: Test the agent with MCP tools + +```bash +python src/agents/anomaly_classification_agent_mcp.py +``` + +Verify that the agent responds with a correct answer using the MCP tools instead of direct database access. + +#### Task 2.5: Review and test the agent in the Foundry portal + +❶ Navigate to the [Foundry portal](https://ai.azure.com). + +> [!TIP] +> Make sure you are using the new Foundry portal experience. You might need to enable it using the toggle in the upper right corner. +> +> New Foundry portal toggle + +❷ Select the **Build** tab to list available agents. + +Foundry portal build tab + +❸ Examine the configuration details for `AnomalyClassificationAgent`. + +Foundry Portal Anomaly Classification Agent + +> [!NOTE] +> There are two versions of the Anomaly Classification Agent: one for the initial agent with local tools, and one for the version that uses MCP tools. + +❹ Select the Anomaly Classification Agent and try out some additional questions in the playground: + +* Normal condition (no maintenance needed). Use the query `Hello, can you classify the following metric for machine-002: [{"metric": "drum_vibration", "value": 2.1}]` + +Foundry Portal Playground + +* Critical anomaly. Use the query `Hello, can you classify the following metric for machine-005: [{"metric": "mixing_temperature", "value": 175}]` + +### Task 3: Understand root cause with the Fault Diagnosis Agent and Foundry IQ + +The **Fault Diagnosis Agent** is tasked with understanding the actual root cause of the issues raised by the Anomaly Classification Agent. Besides machine data and maintenance history, add a machine wiki as a tool for the agent by using Foundry IQ. + +#### Task 3.1: Examine the machine data wiki + +The machine wiki contains knowledge (common issues, repair instructions, and repair details) about different machine types. The wiki pages are available as markdown files in Azure Blob Storage. + +❶ Navigate to the [Azure portal](https://portal.azure.com) and locate the storage account. + +❷ Select **Storage browser** > **Blob containers** and select the `machine-wiki` container. + +❸ Select a wiki article and select the **Edit** tab to preview the content. + +Storage wiki page + +#### Task 3.2: Expose the machine wiki data as a knowledge base + +Foundry IQ consists of knowledge sources (*what* to retrieve) and knowledge bases (*how* to retrieve). Knowledge sources are created as standalone objects and then referenced in a knowledge base. + +> [!NOTE] +> Foundry Agent Service orchestrates calls to the knowledge base via the MCP tool and synthesizes the final answer. At runtime, the agent calls only the knowledge base, not the data platform (Azure Blob Storage in this case) that underlies the knowledge source. The knowledge base handles all retrieval operations. + +Create a knowledge source and knowledge base by following the steps in [`src/create_knowledge_base.ipynb`](../src/create_knowledge_base.ipynb). + +> [!TIP] +> When running the notebook you will be asked to select an environment. Pick the user-installed Python environment. +> +> Select Python environment + +#### Task 3.3: Create the Fault Diagnosis Agent + +Create the Fault Diagnosis Agent and use the newly created Foundry IQ knowledge base. + +Examine the Python code in [`src/agents/fault_diagnosis_agent.py`](../src/agents/fault_diagnosis_agent.py). Currently only one tool, `machine_data`, is available. Your task is to add the knowledge base MCP tool to the agent so the machine wiki content can be used when diagnosing the root cause of the anomaly. + +❶ Locate the placeholder comment `# TODO: add Foundry IQ MCP tool` in `src/agents/fault_diagnosis_agent.py`. + +❷ Add the knowledge base as an `MCPTool` by replacing the placeholder with: + +```python +MCPTool( + server_label="machine-wiki", + server_url=machine_wiki_mcp_endpoint, + require_approval="never", + project_connection_id="machine-wiki-connection" +) +``` + +A few things to observe: + +* The agent now uses two MCP tools: `knowledge_base` retrieves machine wiki information for root cause analysis, and `machine_data` fetches details about machines. +* The agent is clearly instructed to use the machine knowledge base instead of its own knowledge. + +Run the code: + +```bash +python src/agents/fault_diagnosis_agent.py +``` + +Verify that the agent's answer includes a root cause, severity, and a knowledge base reference from the machine wiki. + +🎉 Congratulations! You've successfully built two agents and equipped them with enterprise tools to perform their tasks. + +## 🚀 Go Further + +> [!NOTE] +> Finished early? These tasks are **optional** extras for exploration. Feel free to move on to the next challenge — you can always come back later! + +
+Experiment with prompts + +Try modifying the system instructions in `anomaly_classification_agent_mcp.py` or `fault_diagnosis_agent.py`. Observe how different phrasings affect the agent's responses — this is practical prompt engineering in action. + +
+ +
+Test edge cases in the playground + +Use the Foundry portal playground to test boundary conditions: non-existent machines (for example `machine-999`), metrics at exact threshold values, invalid or malformed input data, and multiple anomalies at once. + +
+ +
+Add a new wiki article + +Create a new markdown file for a machine type (copy and modify an existing article), upload it to the `machine-wiki` container in Azure Blob Storage, re-run the indexer to include the new content, and test retrieval with the Fault Diagnosis Agent. + +
+ +## 🛠️ Troubleshooting + +* **`Connection 'xxx-connection' not found` error when running agents:** this is a known intermittent issue with Foundry's MCP connection resolution (the service is in preview) — the connections exist but are not always resolved correctly by the agent runtime. Run the script again (the error is transient and often succeeds on retry), test in the Foundry portal playground if the agent was already created, or wait a few minutes for connection state to propagate. Verify connections exist with: + + ```bash + az rest --method GET \ + --url "https://management.azure.com${AZURE_AI_PROJECT_RESOURCE_ID}/connections?api-version=2025-10-01-preview" \ + --query "value[].name" -o tsv + ``` + +* **The agent doesn't show up in the Foundry portal:** there can be a delay before newly created agents are visible. If you don't see the agent after 10 minutes, refresh the browser or run the Python script again. +* **`PermissionDenied` when running agent creation scripts:** confirm you have the *Azure AI Developer* role on the Foundry project. + +## 🧠 Conclusion and Reflection + +In Task 1, you created the Anomaly Classification Agent using a Python script. + +Task 1 recap + +The agent had a system prompt with instructions on how to behave, and two *local* tools to query Cosmos DB data. When running the Python script, the tools executed locally in the Python process — if you asked the same questions in the Foundry portal playground, the agent wouldn't be able to answer since the tools are not available there. + +> [!NOTE] +> **When do you need an agent?** This particular example could be solved *without* an agent — it's mainly mapping metrics to thresholds. The only thing AI is used for here is generating a human-readable summary of the issue. However, using an agent (besides being a learning exercise) means you can add more tools in the future for more advanced reasoning, plus built-in observability and memory (conversation history), which you'll examine in Challenge 4. +> +> You don't always need a full agent — you can call a model directly through the Azure AI Inference SDK when your use case is simpler. And agents and traditional code work well together: an agent doesn't have to perform every step in a workflow. Traditional code is often better suited for deterministic logic like validation, filtering, or data transformation. A good rule of thumb: if your agent's system instructions are becoming very long and detailed with step-by-step rules, that's often a sign that some of that logic belongs in code rather than in the prompt. Use agents for reasoning, interpretation, and decisions that benefit from flexibility, and use code for everything else. + +In Task 2, you published the APIs as MCP servers in API Management and connected them to the Foundry project. + +Task 2 recap + +The Anomaly Classification Agent could then run fully in Agent Service, so the questions asked in the Foundry playground worked. + +Finally, in Task 3, you created the Fault Diagnosis Agent and grounded it with data via AI Search exposed as an MCP server. + +Task 3 recap + +This agent also runs fully in Agent Service and can use the tools when answering questions in the playground. Note that the content from Blob Storage isn't fetched on demand — instead, it's indexed ahead of time, and retrieval queries are executed against AI Search. + +If you want to learn more about what you covered in this challenge, check out the links below: + +* [Model Context Protocol](https://modelcontextprotocol.io/) +* [Expose an API as an MCP server in API Management](https://learn.microsoft.com/azure/api-management/export-rest-mcp-server) +* [Foundry IQ overview](https://learn.microsoft.com/azure/ai-foundry/concepts/foundry-iq) +* [Azure AI Search documentation](https://learn.microsoft.com/azure/search/) + +**Next:** [Challenge 3 - Repair Planner Agent and AI-Driven Development](challenge-03.md) diff --git a/03-Azure/01-04-AI/08_Predictive_Maintenance/challenges/challenge-03.md b/03-Azure/01-04-AI/08_Predictive_Maintenance/challenges/challenge-03.md new file mode 100644 index 000000000..7881c4362 --- /dev/null +++ b/03-Azure/01-04-AI/08_Predictive_Maintenance/challenges/challenge-03.md @@ -0,0 +1,382 @@ +# Challenge 3 - Repair Planner Agent and AI-Driven Development + +**[Home](../README.md)** | [Previous challenge](challenge-02.md) | [Next challenge](challenge-04.md) + +> [!TIP] +> Stuck on a step? See the [Challenge 3 walkthrough](../walkthrough/challenge-03/solution-03.md) for exact commands, expected output, and a validation checklist. + +## 🎯 Objective + +The goals for this challenge are: + +* Pair program with GitHub Copilot to build an agent from scratch +* Create a .NET agent using the Foundry Agents SDK +* Understand the similarities between a coding assistant (like GitHub Copilot) and a Foundry Agent — both use instructions, tools, and an LLM to accomplish tasks + +## 🧭 Context and Background + +![Challenge 3 scenario](./images/challenge-03-scenario.png) + +The **Repair Planner Agent** is the third component in the multi-agent system. After a fault has been diagnosed, this agent determines what repair tasks need to be performed, finds technicians with the required skills, checks parts inventory, and creates a structured work order. + +You will implement the Repair Planner Agent as a .NET application that reads information about `Technicians` and `PartsInventory` from Cosmos DB. The final work order is also saved in Cosmos DB. The following diagram illustrates the target solution. + +Challenge 3 Target Solution + +❶ The **Repair Planner** is a .NET console application with `Program.cs` as its entry point. + +❷ The `RepairPlannerAgent.cs` class registers the agent and orchestrates calls to other services. + +❸ The `CosmosDbService.cs` class encapsulates Cosmos DB data access. + +❹ In this exercise, the mappings between faults and required skills/parts are done as a static mapping in `FaultMappingService.cs`. In a real-world application, this would be fetched from another system. + +> [!TIP] +> This is just one way to structure the solution — there are many valid approaches! If you have experience with .NET, feel free to experiment with a different architecture (for example dependency injection, separate class libraries, or different layering patterns). The goal, though, is to have GitHub Copilot write the majority of the code while you provide guidance and review. + +The following mappings are used in this exercise. The `agentplanning` agent already knows these mappings, so you don't need to copy them manually. + +
+Fault → Required Skills + +* `curing_temperature_excessive` → `tire_curing_press`, `temperature_control`, `instrumentation`, `electrical_systems`, `plc_troubleshooting`, `mold_maintenance` +* `curing_cycle_time_deviation` → `tire_curing_press`, `plc_troubleshooting`, `mold_maintenance`, `bladder_replacement`, `hydraulic_systems`, `instrumentation` +* `building_drum_vibration` → `tire_building_machine`, `vibration_analysis`, `bearing_replacement`, `alignment`, `precision_alignment`, `drum_balancing`, `mechanical_systems` +* `ply_tension_excessive` → `tire_building_machine`, `tension_control`, `servo_systems`, `precision_alignment`, `sensor_alignment`, `plc_programming` +* `extruder_barrel_overheating` → `tire_extruder`, `temperature_control`, `rubber_processing`, `screw_maintenance`, `instrumentation`, `electrical_systems`, `motor_drives` +* `low_material_throughput` → `tire_extruder`, `rubber_processing`, `screw_maintenance`, `motor_drives`, `temperature_control` +* `high_radial_force_variation` → `tire_uniformity_machine`, `data_analysis`, `measurement_systems`, `tire_building_machine`, `tire_curing_press` +* `load_cell_drift` → `tire_uniformity_machine`, `load_cell_calibration`, `measurement_systems`, `sensor_alignment`, `instrumentation` +* `mixing_temperature_excessive` → `banbury_mixer`, `temperature_control`, `rubber_processing`, `instrumentation`, `electrical_systems`, `mechanical_systems` +* `excessive_mixer_vibration` → `banbury_mixer`, `vibration_analysis`, `bearing_replacement`, `alignment`, `mechanical_systems`, `preventive_maintenance` + +
+ +
+Fault → Required Parts + +* `curing_temperature_excessive` → `TCP-HTR-4KW`, `GEN-TS-K400` +* `curing_cycle_time_deviation` → `TCP-BLD-800`, `TCP-SEAL-200` +* `building_drum_vibration` → `TBM-BRG-6220` +* `ply_tension_excessive` → `TBM-LS-500N`, `TBM-SRV-5KW` +* `extruder_barrel_overheating` → `EXT-HTR-BAND`, `GEN-TS-K400` +* `low_material_throughput` → `EXT-SCR-250`, `EXT-DIE-TR` +* `high_radial_force_variation` → (empty array) +* `load_cell_drift` → `TUM-LC-2KN`, `TUM-ENC-5000` +* `mixing_temperature_excessive` → `BMX-TIP-500`, `GEN-TS-K400` +* `excessive_mixer_vibration` → `BMX-BRG-22320`, `BMX-SEAL-DP` + +
+ +### Using the custom GitHub Copilot agent (agentplanning) + +GitHub Copilot custom agents in VS Code are reusable, task-specific chat personas. A custom agent bundles a set of instructions (how Copilot should behave) and an allowed set of tools (what Copilot can do). This makes it easy to switch into a consistent "mode" (for example, planning vs. implementation) without re-explaining context each time. In a workspace, custom agents are typically defined as `.agent.md` files under `.github/agents`. + +This repository includes a specialized GitHub Copilot agent called `agentplanning` that knows: + +* Foundry Agents SDK patterns (`Azure.AI.Projects` + `Microsoft.Agents.AI`) +* .NET and C# best practices +* Cosmos DB integration +* The fault → skills/parts mappings for this microhack + +#### Agent-driven development workflow + +Follow this workflow when using the agent planner: + +1. Ask the agent to plan the component architecture. +2. Request code generation with specific requirements. +3. Review and refine the generated code. +4. Ask for improvements or additional features. +5. Request tests to validate functionality. + +## ✅ Tasks + +> [!IMPORTANT] +> The outcome depends on which model GitHub Copilot uses. Larger models may handle more complex prompts. Smaller models work better with focused, single-file requests. + +### Task 1: Project setup + +Create a new empty .NET application that will host your agent. + +```bash +# Navigate to the src directory +cd src + +# Create a new console application +dotnet new console -n RepairPlanner + +# Navigate into the project +cd RepairPlanner +``` + +### Task 2: Create the RepairPlanner agent with agentplanning + +Open GitHub Copilot Chat (Ctrl+Shift+I or Cmd+Shift+I) and select the `agentplanning` agent in the Agents dropdown. + +agentplanning selection + +#### Task 2.1: Architecture planning + +Start with the following prompt to understand the proposed setup for the Repair Planner Agent. + +💬 Ask the agent: + +``` +I need to build a Repair Planner Agent in .NET for Challenge 3 +using the Foundry Agents SDK. Can you explain the architecture? +``` + +#### Task 2.2: Create data models + +Now let the agent create the data models. + +💬 Ask the agent: + +``` +Create all data models for the Repair Planner Agent: +- DiagnosedFault (input from previous agent) +- Technician (with skills and availability) +- Part (inventory items) +- WorkOrder (output with tasks) +- RepairTask (individual repair steps) +- WorkOrderPartUsage (parts needed) + +Use dual JSON attributes for Cosmos DB compatibility. +``` + +#### Task 2.3: Create FaultMappingService + +Create a service for mapping between fault/skills and fault/parts. In this exercise, it will be a static mapping of values, but in a real-world scenario this would be fetched from a dedicated system. + +💬 Ask the agent: + +``` +Create a FaultMappingService that maps fault types to required skills and parts using hardcoded dictionaries. +``` + +#### Task 2.4: Create CosmosDbService + +Let's create the data access service. + +💬 Ask the agent: + +``` +Create a CosmosDbService that: +- Queries available technicians by skills +- Fetches parts by part numbers +- Creates work orders +Include error handling and logging. +``` + +#### Task 2.5: Create the main agent + +💬 Ask the agent: + +``` +Create the RepairPlannerAgent class that orchestrates the entire workflow. +It should register the agent, determine required skills, query technicians and parts, +and save the work order. +``` + +#### Task 2.6: Create the main program + +Finally, let the `agentplanning` agent update `Program.cs` to initialize all services and run a sample fault. + +💬 Ask the agent: + +``` +Update Program.cs to initialize all services, create a sample fault, +and demonstrate the repair planning workflow. +``` + +Your completed project could look similar to this: + +```text +RepairPlanner/ +├── RepairPlanner.csproj +├── Program.cs +├── RepairPlannerAgent.cs +├── Models/ +│ ├── DiagnosedFault.cs +│ ├── Technician.cs +│ ├── Part.cs +│ ├── WorkOrder.cs +│ ├── RepairTask.cs +│ └── WorkOrderPartUsage.cs +└── Services/ + ├── CosmosDbService.cs + ├── CosmosDbOptions.cs + └── FaultMappingService.cs +``` + +### Task 3: Test your agent + +Try out your agent. + +```bash +export $(grep -v '^#' ../../.env | xargs) +dotnet run +``` + +Verify that the agent logs finding available technicians, fetching parts, and saving a work order with a generated work order number. + +🎉 Congratulations! You've built a Repair Planner Agent in .NET using GitHub Copilot. + +## 🚀 Go Further + +> [!NOTE] +> Finished early? These tasks are **optional** extras for exploration. Feel free to move on to the next challenge — you can always come back later! + +
+Expose Cosmos DB queries as agent tools + +In the current implementation, the agent orchestrates calls to Cosmos DB directly through the `CosmosDbService` class. An alternative approach is to expose the database queries as **tools** that the agent can invoke, letting the LLM decide when and how to use them. + +**Why consider this?** + +| Direct code orchestration | Tool-based approach | +|---------------------------|---------------------| +| You control the exact sequence of calls | Agent decides which tools to call and when | +| Predictable but rigid | More flexible — agent can adapt to different inputs | +| Good for well-defined workflows | Good when reasoning is needed to choose actions | + +**How to implement:** + +1. Define local function tools (similar to `get_thresholds` and `get_machine_data` in Challenge 2) that wrap your Cosmos DB queries: `get_technicians_by_skills`, `get_parts_inventory`, and `create_work_order`. +2. Register these functions as tools when creating the agent. +3. Update the agent's system prompt to explain when to use each tool. +4. Let the agent decide which tools to call based on the diagnosed fault. + +**Taking it further:** once you have local tools working, you could expose them as an MCP server (via API Management or a standalone server). This would allow the same tools to be shared across multiple agents and managed independently from the agent code — similar to how you exposed the Machine API in Challenge 2. + +
+ +
+Enhance the Repair Planner Agent + +Once the basic agent works, try adding: + +``` +Add priority calculation based on fault severity +``` + +``` +Add better error handling for when no technicians are available +``` + +``` +Add structured output using AIJsonUtilities.CreateJsonSchema +and ChatResponseFormat.ForJsonSchema for type-safe responses +``` + +
+ +
+Add an MCP server to GitHub Copilot + +In Challenge 2, you saw how agents in Foundry Agent Service can use MCP tools. GitHub Copilot in VS Code also supports MCP servers, letting you extend Copilot's capabilities with custom tools. + +Try adding the Microsoft Learn MCP server to your GitHub Copilot setup: + +1. Open the Command Palette (Ctrl+Shift+P or Cmd+Shift+P). +2. Run **MCP: Add Server**. +3. Select **HTTP** as the server type. +4. Enter `https://learn.microsoft.com/api/mcp` as the URL. +5. Enter `microsoft-learn` as the server ID. +6. Select **Workspace** to add the server to `.vscode/mcp.json`. + +In GitHub Copilot Chat, you can now ask questions that use Microsoft Learn documentation — useful for questions about Azure services, .NET APIs, and other Microsoft technologies. + +
+ +
+Create your own custom GitHub Copilot agent + +In this challenge you used the `agentplanning` agent — a custom GitHub Copilot agent defined in [`.github/agents/agentplanning.agent.md`](../.github/agents/agentplanning.agent.md). Try creating your own custom agent with a specific persona: + +1. Create a new file in `.github/agents/` with a `.agent.md` extension. +2. Define the agent's persona, instructions, and any tools it should use. +3. Test it by selecting your new agent from the Agents dropdown in GitHub Copilot Chat. + +Two agent personas that would be useful in this microhack: + +**🧪 Agent Test Generator** (`agenttester.agent.md`) — a test-driven development specialist for AI agents. Helps write unit tests, integration tests, and mock data for agent workflows. Example prompt: "Write unit tests for the CosmosDbService that mock the container responses." + +**🗄️ Cosmos DB Helper** (`cosmoshelper.agent.md`) — a database specialist focused on Cosmos DB best practices. Helps write efficient queries, design partition keys, and troubleshoot data access. Example prompt: "Write a query to find all technicians with bearing_replacement skill who are available." + +See [Custom agents in VS Code](https://code.visualstudio.com/docs/copilot/customization/custom-agents) for detailed documentation on creating custom agents. + +
+ +## 🛠️ Troubleshooting + +* **Preview API warnings:** add `$(NoWarn);CA2252` to your `RepairPlanner.csproj`. +* **JSON parsing errors with numbers:** LLMs sometimes return `"60"` instead of `60`. Use `NumberHandling = JsonNumberHandling.AllowReadingFromString`. +* **Cosmos DB errors:** ensure you're using both `[JsonPropertyName]` and `[JsonProperty]` attributes on models. +* **Is there a finished example solution available?** Yes — see the [Challenge 3 walkthrough](../walkthrough/challenge-03/solution-03.md), which links to a complete reference implementation. Try building the agent yourself with Copilot's guidance first; only consult the reference if you want to compare your work or are stuck, since `.github/copilot-instructions.md` already asks Copilot not to copy from it directly. + +## 🧠 Conclusion and Reflection + +### C# vs Python + +You used .NET (C#) in this challenge and Python in the previous one — both are first-class for building agents with modern AI/agent SDKs (including the Foundry Agents SDK patterns used in this microhack). Agent solutions quickly become application development (integration, data access, security, ops), so teams typically choose the language that best fits their existing stack and skills — Python often excels for rapid iteration, while .NET is common in larger enterprises for long-lived, well-governed services. + +### GitHub Copilot instructions + +This repository uses VS Code Copilot customization so the agent behaves consistently during the microhack. + +> [!TIP] +> Using guided agents (clear instructions + constrained tools + repeatable steps) is an improvement over pure vibe coding, where solutions can drift, skip requirements, or become hard to review. A lightweight, guided approach keeps changes aligned with the goal and makes agent output easier to validate. + +GitHub Copilot instructions + +The diagram above illustrates how GitHub Copilot combines multiple inputs to generate contextual responses: + +❶ **Your prompt** — the primary instruction you provide to the agent (for example "Create the RepairPlannerAgent class..."). This is your direct request that drives the conversation. + +❷ **Custom instructions** — two types of instruction files shape Copilot's behavior: + +* [`copilot-instructions.md`](../.github/copilot-instructions.md) — general workspace-wide instructions applied to all chat requests (SDK constraints, pinned package versions, environment variables, and so on) +* [`agentplanning.agent.md`](../.github/agents/agentplanning.agent.md) — the specific role and persona for the `agentplanning` agent, discovered by VS Code from `.github/agents/*.agent.md` and selectable from the Agents dropdown + +❸ **Workspace context** — Copilot examines files in your workspace to understand structure and data, such as `README.md`, `technicians.json`, `work-orders.json`, and your existing code files. This helps it generate code that fits your project. + +❹ **Tools and data (MCP)** — Copilot can be equipped with additional tools exposed via the Model Context Protocol (MCP) to accomplish more complex tasks. This is very similar to how the agents in [Challenge 2](challenge-02.md) were equipped with tools — just as you gave the Fault Diagnosis Agent access to Cosmos DB queries and a knowledge base, you can extend Copilot with external data sources and capabilities. + +### Taking it further with Spec Kit + +In this challenge, you used custom instructions and a guided agent to build a single component. But what happens when you need to build entire features or complete applications with AI assistance? + +[Spec Kit](https://github.com/github/spec-kit) is an open-source toolkit that takes AI-assisted development to the next level through Spec-Driven Development (SDD). Instead of generating code directly from prompts, SDD flips the traditional approach: specifications become executable, directly generating working implementations rather than just guiding them. + +**Why this matters for larger projects:** + +| Traditional approach | Spec-Driven Development | +|---------------------|------------------------| +| Code is the source of truth | Specifications are the source of truth | +| Specs drift from implementation | Specs generate implementation | +| One-shot prompt → code | Multi-step: specify → plan → tasks → implement | +| Hard to review AI output | Structured artifacts at each stage | + +Spec Kit provides slash commands that structure AI-assisted development: + +* `/speckit.constitution` — establish project principles and architectural guidelines +* `/speckit.specify` — create functional specifications (focus on *what* and *why*, not *how*) +* `/speckit.plan` — generate technical implementation plans with your chosen tech stack +* `/speckit.tasks` — break down into actionable, parallelizable tasks +* `/speckit.implement` — execute tasks according to the plan + +The key insight is that specifications become living documents that generate code, tests, and documentation, rather than artifacts that are written once and ignored. When requirements change, you update the spec and regenerate, maintaining alignment between intent and implementation. + +This approach is particularly valuable when building complete features or applications (not just single files), working on teams where specifications need review and approval, maintaining long-lived projects where requirements evolve, or wanting reproducible, auditable AI-assisted development. + +If you want to expand your knowledge on what you covered in this challenge, have a look at the content below: + +* [Custom agents in VS Code](https://code.visualstudio.com/docs/copilot/customization/custom-agents) +* [Custom instructions in VS Code](https://code.visualstudio.com/docs/copilot/customization/custom-instructions) +* [Use MCP servers in VS Code](https://code.visualstudio.com/docs/copilot/customization/mcp-servers) +* [Microsoft Learn MCP server](https://github.com/microsoftdocs/mcp) +* [Spec Kit on GitHub](https://github.com/github/spec-kit) +* [Spec-Driven Development blog post](https://developer.microsoft.com/blog/spec-driven-development-spec-kit) + +**Next:** [Challenge 4 - Maintenance Scheduler and Parts Ordering Agents](challenge-04.md) diff --git a/03-Azure/01-04-AI/08_Predictive_Maintenance/challenges/challenge-04.md b/03-Azure/01-04-AI/08_Predictive_Maintenance/challenges/challenge-04.md new file mode 100644 index 000000000..9df7afb1c --- /dev/null +++ b/03-Azure/01-04-AI/08_Predictive_Maintenance/challenges/challenge-04.md @@ -0,0 +1,277 @@ +# Challenge 4 - Maintenance Scheduler & Parts Ordering Agents + +**[Home](../README.md)** | [Previous challenge](challenge-03.md) | [Next challenge](challenge-05.md) + +> [!TIP] +> Stuck on a step? See the [Challenge 4 walkthrough](../walkthrough/challenge-04/solution-04.md) for exact commands, expected output, and a validation checklist. + +## 🎯 Objective + +The goals for this challenge are: + +* Use **agent memory** to maintain context across multiple interactions +* Implement **observability** with Azure AI tracing to monitor and debug agent behavior + +## 🧭 Context and Background + +![Challenge 4 scenario](./images/challenge-04-scenario.png) + +The **Maintenance Scheduler Agent** analyzes work orders and historical maintenance data to find optimal maintenance windows that minimize production disruption. The **Parts Ordering Agent** checks inventory levels for required parts, evaluates supplier performance and lead times, and generates optimized parts orders with cost analysis. + +You will use the Azure resources highlighted in the image below. + +Azure Resources + +Both agents interact with **Cosmos DB** as their primary data store. + +
+Containers used + +| Container | Purpose | Agent usage | +|-----------|---------|-------------| +| `WorkOrders` | Work orders from the Repair Planner | Read by both agents to get job details | +| `Machines` | Equipment information | Referenced for machine context | +| `MaintenanceHistory` | Historical maintenance records | Read by the **Maintenance Scheduler Agent** for pattern analysis | +| `MaintenanceWindows` | Available production windows | Read by the **Maintenance Scheduler Agent** to find optimal timing | +| `MaintenanceSchedules` | Generated maintenance schedules | Written by the **Maintenance Scheduler Agent** | +| `PartsInventory` | Current stock levels | Read by the **Parts Ordering Agent** to check availability | +| `Suppliers` | Supplier information | Read by the **Parts Ordering Agent** for sourcing decisions | +| `PartsOrders` | Generated parts orders | Written by the **Parts Ordering Agent** | + +
+ +
+Data flow + +```text +┌─────────────────────────────────────────────────────────────────────────────┐ +│ MAINTENANCE SCHEDULER AGENT │ +└─────────────────────────────────────────────────────────────────────────────┘ + +Input (READ): Output (WRITE): +├─ WorkOrders ├─ MaintenanceSchedules +├─ MaintenanceHistory │ └─ scheduled_date +├─ MaintenanceWindows │ └─ risk_score (0-100) +└─ Machines │ └─ predicted_failure_probability + │ └─ recommended_action + │ └─ maintenance_window + │ └─ reasoning + └─ WorkOrders (status update to 'Scheduled') + +┌─────────────────────────────────────────────────────────────────────────────┐ +│ PARTS ORDERING AGENT │ +└─────────────────────────────────────────────────────────────────────────────┘ + +Input (READ): Output (WRITE): +├─ WorkOrders ├─ PartsOrders +├─ PartsInventory │ └─ supplier_id, supplier_name +├─ Suppliers │ └─ order_items (part, qty, cost) +└─ Machines │ └─ total_cost + │ └─ expected_delivery_date + │ └─ reasoning + └─ WorkOrders (status update to 'PartsOrdered' or 'Ready') +``` + +
+ +### Agent memory + +Both agents implement **persistent memory** by storing conversation history in **Cosmos DB**. This enables an agent to maintain context across multiple interactions with the same entity (machine or work order). + +Why use agent memory? + +* **Contextual awareness**: an agent can reference previous interactions when making decisions, leading to more informed recommendations. +* **Consistency**: it maintains a coherent conversation thread even when sessions are interrupted or resumed later. +* **Learning from history**: the model can consider past recommendations and outcomes when generating new responses. + +The conversation history is injected into the AI prompt as context, so the model can see what was previously discussed and provide more relevant responses. + +### Azure AI tracing and observability + +Both agents are instrumented for **end-to-end observability** so you can debug runs and understand model behavior without adding print statements everywhere. + +Why observability matters — when running agents in production, you need visibility into what's happening: + +* **Identify bottlenecks**: see which steps take the longest (AI inference vs. database operations). +* **Debug failures**: when an agent fails, traces show exactly which step failed and why. +* **Monitor costs**: token usage metrics help you understand and optimize AI spending. +* **Validate behavior**: confirm that agents are making reasonable decisions by reviewing their reasoning. + +What's included: + +* **Azure Monitor integration** — sends traces to **Application Insights**. +* **AI inference instrumentation** — automatically traces all AI model calls. +* **OpenTelemetry support** — industry-standard distributed tracing. +* **Graceful fallback** — agents work even if tracing packages aren't installed. + +What you get in traces: + +* **Agent execution timeline**: each step (read data, reason, write results). +* **AI model calls**: prompts, responses, token usage, and latency. +* **Cosmos DB operations**: reads/writes to containers used by the agents. +* **Failures with context**: exceptions and which step failed. + +Tracing is automatically enabled when you run the agents, as long as: + +1. Tracing packages are installed (already included in the project dependencies). +2. `APPLICATIONINSIGHTS_CONNECTION_STRING` is set (configured in `.env`). + +The agents use **Azure Monitor** exporters to send traces, metrics, and logs directly to **Application Insights**, which integrates with the **Foundry** portal. + +## ✅ Tasks + +### Task 1: Maintenance Scheduler Agent + +The **Maintenance Scheduler Agent** analyzes work orders and determines the optimal time to perform maintenance by balancing equipment reliability needs against production impact. + +**Agent memory in action:** this agent stores chat history per machine using `get_machine_chat_history` and `save_machine_chat_history`. When the agent runs for the same machine multiple times, it can recall previous maintenance discussions and recommendations, allowing it to build on prior analysis rather than starting fresh each time. + +What it does: + +1. **Reads the work order** from the `WorkOrders` container. +2. **Analyzes historical data** from `MaintenanceHistory` to understand failure patterns. +3. **Checks available windows** from `MaintenanceWindows` to find low-impact periods. +4. **Runs AI analysis** using **Microsoft Agent Framework** to assess risk and recommend timing. +5. **Saves the schedule** to `MaintenanceSchedules` with risk scores and recommendations. +6. **Updates the work order** status to `Scheduled`. + +#### Task 1.1: Run the agent + +```bash +# Ensure you are located in the src directory +cd src + +python agents/maintenance_scheduler_agent.py wo-2024-456 +``` + +Verify that the script prints a predictive maintenance schedule with a risk score, failure probability, recommended action, and reasoning, and that the work order status is updated to `Scheduled`. + +### Task 2: Parts Ordering Agent + +The **Parts Ordering Agent** checks inventory availability and generates optimized parts orders by evaluating supplier reliability, lead times, and costs. + +**Agent memory in action:** this agent stores chat history per work order using `get_work_order_chat_history` and `save_work_order_chat_history`. This allows the agent to maintain ordering context for each job — if a work order is processed multiple times (for example, due to partial fulfillment or changes), the agent remembers previous ordering decisions and can provide consistent recommendations. + +What it does: + +1. **Reads the work order** from `WorkOrders` to get required parts. +2. **Checks inventory** from `PartsInventory` to determine what's in stock. +3. **Identifies missing parts** that need to be ordered. +4. **Finds suppliers** from `Suppliers` that can provide the parts. +5. **Runs AI analysis** to optimize supplier selection based on reliability, lead time, and cost. +6. **Saves the parts order** to `PartsOrders` with order details. +7. **Updates the work order** status to `PartsOrdered` (or `Ready` if all parts are available). + +#### Task 2.1: Run the agent + +```bash +python agents/parts_ordering_agent.py wo-2024-456 +``` + +Verify that the script finds the parts that need ordering, selects a supplier, and generates an order with a total cost and expected delivery date, updating the work order status to `PartsOrdered`. + +Test with another work order where all required parts are already in stock: + +```bash +python agents/parts_ordering_agent.py wo-2024-468 +``` + +Verify that no parts order is needed this time, and the work order status is updated to `Ready` instead. + +### Task 3: Azure AI tracing and observability + +Now let's generate some trace data and explore it in the **Foundry** portal. + +#### Task 3.1: Generate traces + +Generate some trace data by running the batch script [run-batch.py](../src/run-batch.py), which processes five work orders through each agent, generating ten total agent runs. + +```bash +python run-batch.py +``` + +#### Task 3.2: View traces in Foundry + +1. Go to . +2. Select your project. +3. Select **Agents**, then `MaintenanceSchedulerAgent` or `PartsOrderingAgent`. +4. Go to the **Monitor** tab. + +You should see something similar to this: + +Monitor portal + +You can examine metrics for *token usage*, *agent runs*, *tool calls*, and potential *errors*. + +#### Task 3.3: View traces in Application Insights + +The **Foundry** project is connected to **Application Insights**, where the monitoring data is stored. Select **Open in Azure Monitor** in the upper right corner. + +Open in monitor + +This opens **Application Insights**, where you'll see similar monitoring details as in the **Foundry** portal, but you can also drill further into traces for troubleshooting purposes. + +Application Insights + +🎉 Congratulations! You've successfully worked with two agents that integrate with **Cosmos DB** and include production-ready observability. + +## 🚀 Go Further + +> [!NOTE] +> Finished early? These tasks are **optional** extras for exploration. Feel free to move on to the next challenge — you can always come back later! + +
+Inspect conversation history in Cosmos DB + +Open the chat history containers in **Data Explorer** and find the persisted messages for a machine or work order you've run more than once. Notice how prior reasoning is included as context for the next run. + +
+ +
+Compare traces across runs + +Run the same agent against the same work order twice and compare the two traces in Application Insights. Look for differences in token usage, latency, and the reasoning text — how much does agent memory change the second response? + +
+ +
+Add a custom trace attribute + +Extend one of the agents to emit a custom OpenTelemetry span attribute (for example, the machine's criticality level) and confirm it shows up on the trace in Application Insights. + +
+ +## 🛠️ Troubleshooting + +* **No traces show up in the Foundry portal:** confirm `APPLICATIONINSIGHTS_CONNECTION_STRING` is set in `.env` and that the Foundry project is linked to an Application Insights resource. Traces can take a minute or two to appear. +* **`Connection 'xxx-connection' not found` error when running agents:** this is a known intermittent issue with Foundry's MCP connection resolution (the service is in preview) — the connections exist but are not always resolved correctly by the agent runtime. Run the script again, or wait a few minutes for connection state to propagate. +* **Agent memory doesn't seem to carry over between runs:** verify the chat history container has an entry for the specific machine or work order ID — memory is keyed per-entity, so different IDs will not share history. + +## 🧠 Conclusion and Reflection + +In Task 1, you ran the Maintenance Scheduler Agent, which balances equipment reliability against production impact. + +Task 1 recap + +The agent used its memory of prior maintenance discussions for the same machine to inform its recommendation, rather than reasoning about the machine from scratch on every run. + +In Task 2, you ran the Parts Ordering Agent, which evaluates supplier reliability, lead time, and cost to generate an order. + +Task 2 recap + +Notice how the agent produced a different outcome (order parts vs. mark the work order ready) depending purely on the current inventory state read from Cosmos DB. + +Finally, in Task 3, you generated trace data and explored it in the Foundry portal and Application Insights. + +Task 3 recap + +> [!NOTE] +> **Why observability matters for agents:** unlike traditional deterministic code, an agent's behavior depends on the model's reasoning, which can vary between runs. Tracing lets you inspect *why* an agent made a particular decision — not just *whether* it succeeded — which is essential once you move an agentic solution from a demo into production. + +If you want to learn more about what you covered in this challenge, check out the links below: + +* [Azure Monitor OpenTelemetry](https://learn.microsoft.com/azure/azure-monitor/app/opentelemetry-overview) +* [Trace your application with Azure AI Foundry](https://learn.microsoft.com/azure/ai-foundry/concepts/observability) +* [Azure Cosmos DB documentation](https://learn.microsoft.com/azure/cosmos-db/) + +**Next:** [Challenge 5 - Multi-Agent Orchestration](challenge-05.md) diff --git a/03-Azure/01-04-AI/08_Predictive_Maintenance/challenges/challenge-05.md b/03-Azure/01-04-AI/08_Predictive_Maintenance/challenges/challenge-05.md new file mode 100644 index 000000000..e1b3b6b8f --- /dev/null +++ b/03-Azure/01-04-AI/08_Predictive_Maintenance/challenges/challenge-05.md @@ -0,0 +1,352 @@ +# Challenge 5 - Multi-Agent Orchestration with Aspire + +**[Home](../README.md)** | [Previous challenge](challenge-04.md) | [Next: Wrap-up](finish.md) + +> [!TIP] +> Stuck on a step? See the [Challenge 5 walkthrough](../walkthrough/challenge-05/solution-05.md) for exact commands, expected output, and a validation checklist. + +## 🎯 Objective + +The goals for this challenge are: + +* Run an Aspire-hosted **Agent Framework** workflow end-to-end +* Understand how different agent types and hosting models work together +* Observe execution via the Aspire dashboard and traces + +## 🧭 Context and Background + +![Challenge 5 scenario](./images/challenge-05-scenario.png) + +At a high level, each agent contributes a specific capability to the workflow: + +* **Anomaly Classification Agent**: determines whether incoming telemetry indicates an anomaly, and classifies severity. +* **Fault Diagnosis Agent**: interprets the anomaly signals and proposes likely root causes and next checks. +* **Repair Planner Agent**: drafts a repair plan (tasks, parts, and recommended technician actions), with direct access to operational data. +* **Maintenance Scheduler Agent**: selects maintenance windows and assigns technicians based on availability. +* **Parts Ordering Agent**: reserves/requests required parts from inventory or suppliers. + +This challenge demonstrates how a "real application" can orchestrate multiple agents in a workflow — where agents are not the product UI, but capabilities that enhance the solution. + +Now we put all previously used Azure resources in action. + +Azure Resources + +### Why Aspire (workflow host) + +We use **.NET Aspire** as the host for the workflow. Aspire is an opinionated framework for building distributed applications, and it's a good fit here because: + +* It gives a consistent developer experience for running multi-service apps locally. +* It provides a built-in dashboard for observability. +* It supports polyglot solutions (this project includes both .NET and Python components). + +In practice, that means Aspire can orchestrate both .NET services and Python processes side-by-side, which is exactly what we need for this multi-agent workflow. + +In other words, Aspire is the "glue" that runs the workflow host, API, UI, and agent processes together and gives you one place to see logs, health, and traces. For agentic systems, that observability is especially useful because a single user action can fan out into multiple agent calls and tool invocations. + +### Agent-to-Agent (A2A) invocation + +This solution includes different types of agents: + +* **Anomaly Classification Agent** and **Fault Diagnosis Agent** + * Defined in Python + * Running fully in **Foundry Agent Service** (no local execution) +* **Repair Planner Agent** + * Implemented in C# + * Runs with local logic and direct access to data sources like **Cosmos DB** + * Invoked from the workflow using **Agent-to-Agent (A2A)** +* **Maintenance Scheduler Agent** and **Parts Ordering Agent** + * Python agents with local logic + * Invoked with A2A similar to the **Repair Planner Agent** + +**A2A** is a pattern for letting a workflow invoke an agent through a consistent interface, so you can combine independently implemented agents without tightly coupling everything together. + +This matters in a polyglot setup: the workflow can call an agent implemented in a different language (or hosted in a different process) the same way it calls any other agent. It also gives you a clean boundary between orchestration (the workflow) and capabilities (the agents), which makes the system easier to evolve over time. + +#### Publishing agents via Azure API Management + +In Challenge 2, we explored how to use [API Management as an AI gateway](challenge-02.md#api-management-as-ai-gateway) for governing and securing access to AI services. **Azure API Management** can also be used to publish agents as A2A endpoints, enabling governed agent-to-agent communication at scale. + +With **API Management** as an A2A gateway, you get: + +* **Centralized agent discovery**: agents can be registered and discovered through a unified gateway. +* **Security and authentication**: apply consistent authentication, authorization, and rate limiting policies across all agent endpoints. +* **Observability**: monitor agent invocations, latencies, and errors through APIM's built-in analytics. +* **Protocol translation**: APIM can handle protocol differences and provide a consistent A2A interface regardless of the underlying agent implementation. + +This approach is particularly valuable in enterprise scenarios where you need to expose agents to multiple consumers while maintaining governance and control over how agents are accessed and invoked. + +### Agent Framework workflows + +The **Microsoft Agent Framework** provides *orchestrations* — pre-built workflow patterns with specially-built executors that allow developers to quickly create complex workflows by simply plugging in their own AI agents. + +#### Why workflows matter + +Traditional single-agent systems are limited in their ability to handle complex, multi-faceted tasks. By orchestrating multiple agents, each with specialized skills or roles, you can create systems that are more robust, adaptive, and capable of solving real-world problems collaboratively. + +#### Supported orchestrations + +The **Agent Framework** supports several orchestration patterns, each suited to different scenarios: + +| Pattern | Description | Typical use case | +|---------|-------------|-------------------| +| **Concurrent** | A task is broadcast to all agents and processed concurrently | Parallel analysis, independent subtasks, ensemble decision making | +| **Sequential** | Passes the result from one agent to the next in a defined order | Step-by-step workflows, pipelines, multi-stage processing | +| **Group Chat** | Assembles agents in a star topology with a manager controlling the flow of conversation | Iterative refinement, collaborative problem-solving, content review | +| **Magentic** | A variant of group chat with a planner-based manager (inspired by MagenticOne) | Complex, generalist multi-agent collaboration | +| **Handoff** | Assembles agents in a mesh topology where agents can dynamically pass control based on context without a central manager | Dynamic workflows, escalation, fallback, or expert handoff scenarios | + +This challenge uses the **Sequential** pattern — see [Conclusion and Reflection](#-conclusion-and-reflection) for why that fits this scenario. + +## ✅ Tasks + +> [!IMPORTANT] +> This workflow expects the **Anomaly Classification** and **Fault Diagnosis** agents to be hosted in **Foundry Agent Service**. Make sure you've completed [Challenge 2](challenge-02.md) before starting the workflow. + +### Task 1: Start Aspire + +```bash +cd src/agent-workflow +aspire run +``` + +### Task 2: Make the workflow API port public (Codespaces) + +The workflow API runs on port `5231`. In VS Code / **GitHub Codespaces**: + +![Port opening](./images/challenge-05-ports.png) + +* Open the *Ports* tab +* Find port `5231` +* Set *Port Visibility* to *Public* + +> This allows the frontend to call the API from the browser. + +### Task 3: Open the frontend + +Once Aspire is running, it provides links in the output. + +* Click the *frontend* link — you should see an app similar to the screenshot below + +![Factory Agent Workflow UI overview](./images/challenge-05-frontend-link.png) + +* Click the link (using Alt+Click) + +The Factory Workflow UI opens in your browser: + +![Factory Workflow UI start](./images/challenge-05-agent-workflow-ui-start.png) + +### Task 4: Run the workflow + +Click the *Trigger Anomaly* button. In the frontend, watch the *Agent Workflow* panel as the workflow progresses through: + +* **Anomaly Classification Agent** +* **Fault Diagnosis Agent** +* **Repair Planner Agent** +* **Maintenance Scheduler Agent** +* **Parts Ordering Agent** + +![Factory Workflow UI in progress](./images/challenge-05-agent-workflow-ui-inprogress.png) + +You should see steps move through states (queued/working/done/error). You can expand the tool call details and agent output: + +![Factory Workflow UI toolcall](./images/challenge-05-agent-workflow-ui-toolcall.png) + +When finished, you should see something similar to this: + +![Factory Agent Workflow finished](./images/challenge-05-agent-workflow-ui-end.png) + +### Task 5: View the dashboard + +Aspire includes a dashboard to observe running services. + +* In the Aspire output, click the *dashboard* link (using Alt+Click) + +![Dashboard link](./images/challenge-05-dashboard-link.png) + +The dashboard starts in your browser and shows the resources: + +![Dashboard start](./images/challenge-05-dashboard-resources.png) + +* Click *Console* to see the invocation chain in the workflow + +![Dashboard console](./images/challenge-05-dashboard-console.png) + +* Click *Traces* to see call duration details + +![Dashboard traces](./images/challenge-05-dashboard-traces.png) + +🎉 Congratulations! You've successfully completed the final challenge. + +## 🚀 Go Further + +> [!NOTE] +> Finished early? These tasks are **optional** extras for exploration. + +
+Add an Executive Summary Agent to the workflow + +Create a new **ExecutiveSummaryAgent** that takes the combined output from the final agent in the workflow and produces a concise, business-friendly summary of the entire maintenance operation. + +What to explore: + +* Create the agent in **Foundry Agent Service** (using a Python script or the Foundry portal UI). +* Update the Aspire workflow code to chain this new agent as the **last step** in the sequential orchestration. +* Consider what information from the previous agents would be most valuable in an executive summary. + +This task exercises your understanding of how to extend an existing Aspire-hosted workflow with additional agents. + +
+ +
+Build a declarative workflow in the Foundry portal + +Use the **Foundry portal** to create a declarative workflow that combines multiple agents without writing orchestration code. + +What to explore: + +* Use the existing **AnomalyClassificationAgent** and **FaultDiagnosisAgent** (already deployed in Agent Service). +* Create a new **ExecutiveSummaryAgent** that summarizes the findings from the previous two agents. +* Stitch all three agents together into a single declarative workflow using the Foundry portal UI. + +This task lets you compare the declarative (portal-based) approach to workflow definition versus the programmatic (Aspire-hosted) approach you used in this challenge. + +
+ +
+Deploy to Azure Container Apps + +In this challenge we ran Aspire locally for development, but the same pattern can be deployed as a hosted application. The lab environment includes both an **Azure Container Registry** and a **Container Apps Environment**. + +Try packaging the workflow host and frontend, and deploying them: + +1. Build container images for the workflow services. +2. Push images to **Azure Container Registry**. +3. Deploy to **Azure Container Apps**. +4. Configure environment variables for the deployed services. + +
+ +
+Explore the Aspire dashboard traces + +The Aspire dashboard provides detailed distributed traces. Try: + +* Clicking on individual trace spans to see timing details. +* Following a request through multiple services. +* Identifying bottlenecks in agent invocations. + +
+ +
+Add custom telemetry + +Extend the workflow with custom telemetry: + +* Add custom metrics for agent response times. +* Create custom spans for specific operations. +* Export traces to **Application Insights**. + +
+ +## 🛠️ Troubleshooting + +* **`aspire` / `aspire run` is not found:** install it and restart your shell session: + + ```bash + curl -fsSL https://aspire.dev/install.sh | bash -s + ``` + + If you're using a `.env` file, load it into your current shell session with `export $(cat .env | xargs)`. + +* **HTTP 401 / `PermissionDenied` when calling model endpoints:** this usually means your identity is missing **Azure OpenAI** data-plane permissions on the **Azure OpenAI** resource. Ensure you have **Cognitive Services OpenAI Contributor** (or **Cognitive Services OpenAI User**) assigned at the resource scope. +* **The frontend loads, but calls to `/api` fail:** confirm port `5231` is forwarded and set to *Public* in the *Ports* view, confirm the frontend is pointing at the correct API URL, and check the Aspire dashboard logs for CORS or network errors. +* **The workflow can't find your Foundry project endpoint:** make sure `AZURE_AI_PROJECT_ENDPOINT` is set in the shell that runs `aspire run`: + + ```bash + echo "$AZURE_AI_PROJECT_ENDPOINT" + ``` + +## 🧠 Conclusion and Reflection + +You've now run an end-to-end, polyglot agent workflow hosted with Aspire. Let's reflect on how the different pieces fit together. + +### Sequential orchestration in this challenge + +In this challenge, we used a **sequential orchestration** to process telemetry anomalies. The workflow passes results from one agent to the next in a defined order: + +```text +Anomaly Classification → Fault Diagnosis → Repair Planner → Maintenance Scheduler → Parts Ordering +``` + +This linear flow makes sense for our scenario because: + +1. **Anomaly Classification** must first determine *if* there's a problem and its severity. +2. **Fault Diagnosis** needs the classification result to identify the root cause. +3. **Repair Planner** requires the diagnosis to create an appropriate repair plan. +4. **Maintenance Scheduler** uses the repair plan to schedule the work. +5. **Parts Ordering** needs the scheduled work order to reserve required parts. + +Each step builds on the previous one — you can't schedule maintenance without knowing what needs to be fixed, and you can't order parts without knowing when the work will happen. The sequential orchestration ensures this dependency chain is respected while keeping the workflow definition simple and readable. + +### Workflow architecture + +![Challenge 5 workflow architecture](./images/challenge-05-workflow-architecture.png) + +The diagram above illustrates the full workflow in nine steps: + +❶ A user triggers a sample anomaly in the Aspire **Factory Agent UI** (sample machine + sample metrics). + +❷ The **agent framework workflow** (implemented in .NET) is triggered. + +❸ The workflow invokes the **Anomaly Classification Agent** running in **Foundry Agent Service**. + +❹ The **Anomaly Classification Agent** uses remote MCP tools to call **API Management** and fetch required data. + +❺ The workflow invokes the **Fault Diagnosis Agent** running in **Foundry Agent Service**. + +❻ The **Fault Diagnosis Agent** uses remote MCP tools to call **API Management** and query **Azure AI Search**. + +❼ The workflow invokes the **Repair Planner Agent** locally via A2A, running as a .NET application. It still uses the agent registration in **Foundry**, but executes its logic locally. + +❽ The **Repair Planner Agent** queries **Cosmos DB** locally to retrieve operational data. + +❾ The final two agents (**Maintenance Scheduler Agent** and **Parts Ordering Agent**) are implemented in Python and also run locally in a separate process (similar to the **Repair Planner Agent**). They're invoked via A2A and query **Cosmos DB** directly. + +In this challenge, you built and ran a solution where agents are **part of the application**, not the entire application. + +* **Choosing different tech stacks is a feature, not a bug**: you can implement agents in Python or C# depending on what they need to do (data access, performance, existing libraries, team skills). +* **Agents can be polyglots**: different agents can be built in different languages and still work together through standard invocation patterns (like A2A). +* **This is close to traditional application development**: you still assemble services, define interfaces, configure environments, and diagnose issues with logs and traces — agents simply enhance the solution's capabilities. + +### Alternative: DevUI for lightweight workflow visualization + +In this challenge we used **Aspire** as the workflow host, which provides comprehensive orchestration and observability for multi-service applications. However, if you only need simple workflow visualization during development — without the full Aspire infrastructure — the **Agent Framework** also includes **DevUI**. + +**DevUI** is a lightweight development tool that provides: + +* Real-time visualization of agent workflow execution +* Inspection of agent inputs, outputs, and tool calls +* A simpler setup for quick iteration during development + +DevUI is ideal when you're focused on debugging a single agent or workflow and don't need the full distributed tracing and service orchestration that Aspire provides. + +If you want to expand your knowledge on what we've covered in this challenge, have a look at the content below: + +* [Microsoft Agent Framework Workflows](https://learn.microsoft.com/agent-framework/user-guide/workflows/overview) +* [What is .NET Aspire?](https://aspire.dev/get-started/what-is-aspire/) +* [Agent-to-Agent in Foundry](https://learn.microsoft.com/azure/ai-foundry/agents/how-to/tools/agent-to-agent?view=foundry&pivots=python) +* [Agent-to-Agent API in Azure API Management](https://learn.microsoft.com/azure/api-management/agent-to-agent-api) +* [DevUI for Agent Framework](https://learn.microsoft.com/agent-framework/user-guide/devui/?pivots=programming-language-csharp) + +--- + +You've now completed the final challenge of the Predictive Maintenance multi-agent workflow hack. You have a complete, end-to-end agentic system that combines: + +* **🤖 AI-powered agents** for anomaly detection, diagnosis, and planning +* **🔌 MCP integration** for governed, extensible tool connectivity +* **📊 Observability** with Aspire dashboard and traces to monitor and debug execution +* **🖥️ A web frontend** for business-user interaction and workflow visualization + +Your system is ready to be extended with additional agents and tools, integrated with other enterprise systems, and deployed to a hosting environment (for example, as a containerized application). Great work! + +**Next:** [Wrap-up](finish.md) diff --git a/03-Azure/01-04-AI/08_Predictive_Maintenance/challenges/finish.md b/03-Azure/01-04-AI/08_Predictive_Maintenance/challenges/finish.md new file mode 100644 index 000000000..999452baa --- /dev/null +++ b/03-Azure/01-04-AI/08_Predictive_Maintenance/challenges/finish.md @@ -0,0 +1,9 @@ +# Finish + +[Previous Challenge](challenge-05.md) - **[Home](../README.md)** + +## Congratulations + +You finished the Predictive Maintenance MicroHack. We hope you learned how to design and implement a production-ready, multi-agent predictive maintenance solution — combining Foundry Agent Service, MCP tooling, Foundry IQ, agent memory and observability, and a polyglot Aspire-hosted workflow. + +Thank you for investing the time, and see you next time! diff --git a/03-Azure/01-04-AI/08_Predictive_Maintenance/challenges/images/agentic-maintenance-orchestration.png b/03-Azure/01-04-AI/08_Predictive_Maintenance/challenges/images/agentic-maintenance-orchestration.png new file mode 100644 index 0000000000000000000000000000000000000000..9b9f23b3f3465bfb8990c22a23fd4710466f7e76 GIT binary patch literal 480416 zcmeFZXIPV2{s!#qthysA0|=74^bYILq>D-oMY?oD?>M>$p$bTmsvy1hPAou> z0FmB_5Fm620aAeQp2yvF_5b~SKfPYp4DHF2^PKam_kI6@xTmLa;oQ}8r%s)^pm|sI zKKSB#>eLzUf1Clo=_#y)zWm~KU*peH6$9+6;D^6E-qF2t>Qr?+-I4Wa@bhm^?wWd? zI>o{Z{r8LJ{p&m61rbgUO}tHXb>wV4+=Q*|JZ$WR{oS5`t52O$Q1*XfZR=w1{fCXc zqm#QL|8^^q{|_fSMSf#R-P^iPRO}x+-3|1#e-Nnm&^FM;R@RPRS?R(#1%Ekk2RD0f 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