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Meta Agent OS

A Markdown-based operating system for diagnosing, designing, costing, building, testing, and operating multi-agent AI systems.

Current version: v0.6.1 (Inference Safety Layer)
License: Apache 2.0


What It Is

Meta Agent OS is a structured methodology and knowledge management system that lives upstream of code. It is not a runtime framework. It does not execute agents. It gives you a repeatable process for designing agent systems that are worth building.

The core insight: most multi-agent projects fail because teams build before they diagnose. Meta Agent OS enforces diagnosis first.

For the deeper product boundary, see docs/WHY.md, docs/PUBLIC_BOUNDARY.md, and docs/RUNTIME_TRACK.md.

Current Status

Meta Agent OS v0.6.1 is complete as a specification: the methodology, control files, schemas, and validation are in place. Adoption is unproven — no non-author has run it end to end, and the standing Evaluator verdict is "ready for internal use, not yet ready for broad public launch." It is not permanently finished, but the current repo should remain focused on methodology, control files, examples, validation, and memory.

Truly agentic runtime behavior should be explored in a separate implementation track rather than added directly to this public repo by default.


How It Works

Ten specialist personas progress through ordered stages. Each stage produces a structured Markdown output. Human decision gates prevent unreviewed advancement.

Oracle -> Sophia -> Cartographer -> Architect -> QS -> Guardian -> Builder -> Evaluator -> Orchestrator -> Librarian
Stage Persona Output
1 The Oracle Diagnosis - project purpose, gaps, constraints, true needs
2 Sophia Research - evidence, competitive analysis, positioning
3 The Cartographer Workflow map - stakeholders, tools, data, handoffs
4 The Architect System design variants - agents, roles, architecture options
5 The QS Cost and implementation plan - tokens, effort, phased build
6 The Guardian Risk review - security, privacy, permissions, failure modes
7 The Builder Implementation - prompts, configs, workflows, scripts
8 The Evaluator QA and evals - acceptance criteria, test cases, launch readiness
9 The Orchestrator Runtime - routing, handoffs, execution, improvement loops
10 The Librarian Memory - decisions, assumptions, changelog, reusable patterns

Quick Start

Claude Code

Copy this repository into your project root. Then run:

/mao-init

to reset state for your new project, followed by:

/mao-diagnose

to start the first stage.

Operator shortcuts are also available:

Command Purpose
/mao-status Inspect current stage, missing outputs, blockers, and next action
/mao-diagnose Run or refresh Oracle diagnosis
/mao-resume Continue from the current incomplete stage
/mao-validate Validate current state and current stage output
/mao-harden Check and repair repository structure
/mao-memory Update project brain, assumptions, decisions, and changelog
/mao-export-pack Verify the framework is ready to reuse in another repo

Codex or Other Coding Agents

Read AGENTS.md and CODEX_RUNBOOK.md, then run the Codex Full Run Prompt v0.4 from /meta-agent-os/00_control/codex/full_run_v0_4.md.

Manual Trigger

Read CLAUDE.md, AGENTS.md, and META_AGENT_BOOTSTRAP.md. Run The Oracle diagnosis stage for this repository.

Repository Structure

/meta-agent-os
  /00_control       - run manifests, stage state, schemas, validators, modes
  /03_outputs       - stage outputs (diagnosis, research, maps, architecture, costs, risk, build, evals, runtime)
  /05_memory        - project brain, decision log, assumptions log, changelog

/docs               - positioning, boundaries, release checklist, repo hardening
/docs/examples      - worked examples (support triage, research assistant)
/docs/notes         - dated working notes, kept out of the repository root
/scripts            - validation scripts (PowerShell and bash, behaviourally paired)
/skills             - reusable skill adapter

CLAUDE.md           - instructions for Claude Code
AGENTS.md           - instructions for Codex and other coding agents
CODEX_RUNBOOK.md    - Codex-specific trigger manual
META_AGENT_BOOTSTRAP.md - first-run bootstrap instructions
INSTALL.md          - installation guide
ROADMAP.md          - planned milestones
CONTRIBUTING.md     - what belongs here, and how to run validation
SECURITY.md         - how to report a vulnerability privately
CODE_OF_CONDUCT.md  - community standards

Non-Negotiables

  • Diagnose before building.
  • Do not modify application code during first diagnosis.
  • Separate facts from assumptions.
  • Record major decisions and assumptions.
  • Require human approval for external actions, financial changes, or irreversible operations.
  • Add evals before treating any agent system as production-ready.

Worked Example

Start with docs/examples/WORKED_EXAMPLE_SUPPORT_TRIAGE.md to see how a vague support automation idea becomes a supervised, risk-reviewed agent-system plan.

For a second pattern, see docs/examples/WORKED_EXAMPLE_RESEARCH_ASSISTANT.md, which shows how Meta Agent OS keeps a research-agent request grounded in sources, review gates, and citation discipline.

Release Readiness

Use docs/RELEASE_CHECKLIST.md before tagging, publishing, or reusing this repo as a baseline.

Local validation:

./scripts/check-meta-agent-os.ps1 -Strict
./scripts/check-meta-agent-os.ps1 -Strict -Json
./scripts/check-meta-agent-os.sh --strict
./scripts/check-meta-agent-os.sh --strict --json

Strict mode goes beyond checking that files and headings exist. It also verifies that required sections carry real content, that Assumptions, Risks, Open Questions, and file lists enumerate entries rather than gesture at them, that no unresolved placeholders remain in prose, that completed stage outputs carry **Date:** and **Status:** provenance, that state is not older than the outputs it describes, and that every relative Markdown link resolves.


Should This Be An Agent?

The Oracle stage runs an anti-agent gate before any design work. Its default answer is no; an agent has to be argued for.

python scripts/agent-suitability.py YOUR_ASSESSMENT.json

Four conditions must all hold, and six dimensions are scored with mandatory justifications. tolerance_for_error and recoverability are critical: a zero on either caps the workflow at Not Suitable no matter how valuable it is. The tool exits non-zero when it blocks, so STAGE_GATES.md can enforce it rather than rely on someone remembering.

Concluding that a workflow does not need agents is a valid and valuable output. See ANTI_AGENT_GATE.md and AGENT_SUITABILITY_SCORING.md.

Model choice carries its own risks, covered in INFERENCE_RISK_PROFILES.md: task criticality tiers, model quality floors, local-model limits, and escalation paths. A cost saving that drops a step below its tier floor is not a saving.


Costing An Agent Workflow

The QS stage estimates what a workflow costs to run before it is built.

python scripts/roi-calculator.py YOUR_WORKFLOW.json

The calculation, its thresholds, and what it deliberately does not price are defined in meta-agent-os/00_control/economics/ROI_METHOD.md. Start from TOKEN_BUDGET_TEMPLATE.md for the input shape, and docs/examples/roi/ for a runnable synthetic example.

Supervision cost is inside the running cost, and a baseline is mandatory. Both are how agent proposals usually overstate their return.

Meta Agent OS ships no model prices. Published rates change, and a stale rate produces a confident wrong number. Populate MODEL_PRICING.json with rates you verified today; strict validation rejects any entry that is undated, unsourced, or older than max_age_days.


Contributing

See CONTRIBUTING.md for what belongs in this repository and what is routed to the separate runtime track. Report security issues privately per SECURITY.md. Participation is governed by CODE_OF_CONDUCT.md.


Supported Tools

Tool Primary File Trigger
Claude Code CLAUDE.md /mao-* (see TRIGGER_INDEX.md)
Codex AGENTS.md + CODEX_RUNBOOK.md Prompt commands
Cursor / generic agent AGENTS.md Prompt commands

Versions

Version Name Key Addition
v0.1 Meta Agent Framework Ten personas, ten stage outputs
v0.2 Agentic Runner Stage state, gates, resume protocol, Claude commands
v0.4 Reliability Layer JSON state, schemas, validators, quality bar, run modes
v0.5 Public Framework Polish Positioning docs, worked examples, substance-level validation, link and provenance checks, community guidelines
v0.6 Inference Economics Layer Token budgets, ROI method and calculator, verified-pricing registry, extended QS stage
v0.6.1 Inference Safety Layer Anti-agent gate, suitability scoring, suitability and ROI gates before Architect, model quality floors

Earlier full-tree snapshots are preserved as git tags rather than in-tree folders. Retrieve one with git checkout v0.1 (or v0.2 / v0.4 / v0.5 / v0.6 / v0.6.1), or pull a single file with git checkout v0.6.1 -- <path>.

See ROADMAP.md for planned milestones.


License

Apache 2.0. See LICENSE.

Vertical domain packs (Consulting, Financial Services, Healthcare, Legal) are distributed under separate commercial licenses.

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A Markdown-based operating system for diagnosing, designing, costing, building, and operating multi-agent AI systems. Methodology upstream of code.

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