AI Release Readiness Checklist

A practical, risk-tiered checklist framework for evaluating AI release readiness, with a packaged CLI validator for local use and CI pipelines.

AI systems need release readiness checks that go beyond ordinary software quality gates. Model behaviour, fallback paths, observability, and accountability all require explicit verification.

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How it works

Three risk tiers, chosen based on safety impact, regulatory exposure, and reversibility:

Tier	Use when
Low risk	Internal tools, no safety impact, easily reversible
Medium risk	Customer-facing, some regulatory context, limited fallback
High risk	Safety-critical, regulated environment, hard to reverse

Higher tiers include all requirements from lower tiers, plus additional items.

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Quick start

git clone https://github.com/simaba/release-checklist.git
cd release-checklist
python -m pip install -e .

Validate an example configuration:

release-checklist validate configs/medium-risk-example.yaml

Generate a report:

release-checklist report configs/medium-risk-example.yaml --format markdown

Create a starter template:

release-checklist init --industry healthcare

Legacy direct execution is still supported for local source checkouts:

python src/check_release.py validate configs/medium-risk-example.yaml

Install development dependencies:

python -m pip install -e ".[dev]"

Example output:

✅ Release readiness check PASSED

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Repository structure

checklists/
  low-risk.md               # Checklist for low-risk AI features
  medium-risk.md            # Checklist for medium-risk AI features
  high-risk.md              # Checklist for high-risk AI features
configs/
  medium-risk-example.yaml  # Example YAML configuration
  high-risk-example.yaml    # Example YAML configuration
src/
  airc/
    cli.py                  # Packaged CLI entry point
    validator.py            # Core validation logic
    report.py               # Text / JSON / Markdown reporting
  check_release.py          # Legacy compatibility wrapper
tests/
  test_validator.py
requirements.txt
pyproject.toml

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Customising for your team

1. Fork the repository.
2. Edit the checklist .md files to match your organisation's requirements.
3. Update the YAML configs to reflect your feature's risk profile.
4. Run release-checklist validate as part of your release pipeline.

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Companion repositories

• AI Release Governance Framework for the broader framework this checklist operationalises.
• Enterprise AI Governance Playbook for where this checklist fits in the full operating lifecycle.

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Related repositories

This repository is part of a connected toolkit for responsible AI operations:

Repository	Purpose
Enterprise AI Governance Playbook	End-to-end AI operating model from intake to improvement
AI Release Governance Framework	Risk-based release gates for AI systems
AI Release Readiness Checklist	Risk-tiered pre-release checklists with CLI tool
AI Accountability Design Patterns	Patterns for human oversight and escalation
Multi-Agent Governance Framework	Roles, authority, and escalation for agent systems
Multi-Agent Orchestration Patterns	Sequential, parallel, and feedback-loop patterns
AI Agent Evaluation Framework	System-level evaluation across 5 dimensions
Agent System Simulator	Runnable multi-agent simulator with governance controls
LLM-powered Lean Six Sigma	AI copilot for structured process improvement

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Shared in a personal capacity. Open to collaborations and feedback. Connect on LinkedIn or Medium.