GAAI — Governed Agentic AI Infrastructure

A .gaai/ folder you drop into any git project. Markdown, YAML and bash, with a vendored Python runtime and a few Node helpers. No SDK. No package to install. No external services.

GAAI turns AI coding tools into reliable agentic software delivery systems.

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See It in Action

You:        /gaai-discover
Discovery:  "What do you want to build?"
You:        "Add rate limiting — 100 req/min per user, 429 on exceeded."
Discovery:  "Got it. Checking memory for existing middleware patterns..."
            → Generates Epic E03 + Story E03S01 with acceptance criteria
            → Runs validation: artefact complete, criteria testable, no scope drift
            → Adds to backlog: status: refined
Discovery:  "Done. E03S01 is ready. Run /gaai-deliver when you're ready."

You:        /gaai-daemon
            → Launches the Delivery Daemon (polls backlog, delivers in parallel via tmux)
Delivery:   → Reads E03S01 from backlog
            → Loads middleware conventions from memory
            → Planning Sub-Agent: produces execution plan
            → Implementation Sub-Agent: adds rate-limiting middleware
            → QA Sub-Agent: all acceptance criteria PASS
            → Story marked done, PR merged to staging
Delivery:   "E03S01 complete. No further Stories in backlog."

Two slash commands. Two isolated contexts. Discovery reasons — it never executes. Delivery executes — it never decides scope. They never share a context window — Delivery runs as a separate OS process (claude -p via tmux), so system prompts can't contaminate each other. The backlog is the contract between them.

/gaai-deliver delivers a single Story in the current session. /gaai-daemon launches a background daemon that polls the backlog and delivers multiple Stories in parallel (each in its own tmux session). See Delivery Daemon →

Full walkthrough in Quick Start

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Why GAAI

AI coding tools are fast — but without governance, speed creates drift: agents touch code they shouldn't, forget decisions from prior sessions, and ship features no one can verify against criteria. GAAI adds the missing layer.

Built for developers who already have product clarity — solo founders, senior engineers, small teams who know what to build and need an agent that ships it reliably without going off-script. If you've ever said "the agent broke something it wasn't supposed to touch," this is for you.

vs.	Difference
AGENTS.md / cursor rules	Solves one session. GAAI adds cross-session memory, scope authorization, and structured delivery.
BMAD-METHOD	Simulates a multi-agent Agile team. GAAI is lighter on Discovery, more rigid on Delivery governance.
LangGraph / AutoGen / CrewAI	Code-first orchestration for building AI systems. GAAI governs the use of AI coding tools. Different abstraction level.
Spec Kit (GitHub)	Spec-driven pipeline (spec → plan → tasks → implement). GAAI adds governance enforcement, multi-agent delivery with QA gates, structured cross-session memory, and automated daemon delivery.

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How It Works

Discovery — you talk to the Discovery Agent in your current session. Clarify what to build. Output: a Story with acceptance criteria in the backlog. Discovery reasons. It does not execute.