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Move Knative Functions to accepted projects (LFX 2026 Term 2) #1878

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nate-double-u merged 1 commit into from
May 6, 2026
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Move Knative Functions to accepted projects (LFX 2026 Term 2) #1878

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26 changes: 26 additions & 0 deletions programs/lfx-mentorship/2026/02-Jun-Aug/README.md
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Expand Up @@ -47,6 +47,8 @@ Mentee application instructions can be found on the [Program Guidelines](https:/
- [Documentation improvements for OIDC and JWT IdPs integrations](#documentation-improvements-for-oidc-and-jwt-idps-integrations)
- [Kmesh](#kmesh)
- [Integrating Kmesh into Headlamp UI](#integrating-kmesh-into-headlamp-ui)
- [Knative Functions](#knative-functions)
- [End-to-End Agentic Workflow for Serverless Functions](#end-to-end-agentic-workflow-for-serverless-functions)
- [krkn - Chaos](#krkn---chaos)
- [Automated Documentation Sync Bot for Krkn-Chaos Projects](#automated-documentation-sync-bot-for-krkn-chaos-projects)
- [Dynamic Cluster-Aware Configuration Generation for Krkn-AI](#dynamic-cluster-aware-configuration-generation-for-krkn-ai)
Expand Down Expand Up @@ -341,6 +343,30 @@ CNCF - Kmesh: Integrating Kmesh into Headlamp UI (2026 Term 2)
- Upstream Issue: https://github.com/kmesh-net/kmesh/issues/1658
- LFX URL: https://mentorship.lfx.linuxfoundation.org/project/c5b8d1fa-b75a-4f88-a63b-e6487dc7e39b

### Knative Functions

#### End-to-End Agentic Workflow for Serverless Functions

CNCF - Knative Functions: End-to-End Agentic Workflow for Serverless Functions (2026 Term 2)

- Description: Knative Functions ships an MCP (Model Context Protocol) server that exposes its toolchain to AI agents. This project builds on that foundation: deepening the MCP server to cover initial environment setup and CI/CD integration, and authoring a companion skill that walks agents through the full lifecycle of a Function. Together these enable end-to-end agentic usage of Serverless Functions; from initial scaffolding through deployment with CI/CD.
- Expected Outcome:
- New agentic installer
- New MCP operations covering prerequisite checks with guidance.
- A companion skill that composes initialization, CI/CD setup, and deployment into a guided workflow.
- End-to-end demonstration.
- User-facing and agent-facing documentation.
- Recommended Skills:
- Familiarity with the Go programming language (ideal) or Python (secondarily).
- Experience with AI/ML agents and interest in programmatic LLM integrations.
- Familiarity with Kubernetes, Serverless, GitOps, CI/CD systems a plus.
- Strong communication skills, with the ability to research and document clearly.
- Mentor(s):
- Luke Kingland (@lkingland, kingland@redhat.com) - Primary
- David Fridrich (@gauron99, dfridric@redhat.com)
- Upstream Issue: https://github.com/knative/func/issues/3646
- LFX URL: https://mentorship.lfx.linuxfoundation.org/project/26086dfb-1c88-487c-a2de-e11cfc857c1a

### krkn - Chaos

#### Automated Documentation Sync Bot for Krkn-Chaos Projects
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29 changes: 0 additions & 29 deletions programs/lfx-mentorship/2026/02-Jun-Aug/project_ideas.md
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Expand Up @@ -18,32 +18,3 @@

## Proposed Project ideas

### Knative Functions

#### End-to-End Agentic Workflow for Serverless Functions

CNCF - Knative Functions: End-to-End Agentic Workflow for Serverless Functions (2026 Term 2)

- Description: Knative Functions ships an MCP (Model Context Protocol) server that exposes its toolchain to AI agents. This project builds on that foundation: deepening the MCP server to cover initial environment setup and CI/CD integration, and authoring a companion skill that walks agents through the full lifecycle of a Function. Together these enable end-to-end agentic usage of Serverless Functions; from initial scaffolding through deployment with CI/CD.

- Expected Outcome:
- New agentic installer
- New MCP operations covering prerequisite checks with guidance.
- A companion skill that composes initialization, CI/CD setup, and deployment into a guided workflow.
- End-to-end demonstration.
- User-facing and agent-facing documentation.

- Recommended Skills:
- Familiarity with the Go programming language (ideal) or Python (secondarily).
- Experience with AI/ML agents and interest in programmatic LLM integrations.
- Familiarity with Kubernetes, Serverless, GitOps, CI/CD systems a plus.
- Strong communication skills, with the ability to research and document clearly.

- Mentor(s):
- Luke Kingland (@lkingland, kingland@redhat.com) - Primary
- David Fridrich (@gauron99, dfridric@redhat.com)

- Upstream Issue: https://github.com/knative/func/issues/3646

- LFX URL:

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