agent-project-governance

AI project governance is enforced by mapping hard constraints into measurable gates and validating conformity through project-scale harness checks. This framework records evidence and ensures deployments remain auditable across iterations.

Tracking AI backlog iterations requires executable slices with traceability from Epics to Stories alongside ADRs. This approach standardizes governance assets and maintains an auditable record of project decisions and progress.

Setting up AGENTS.md and manifest.yaml involves locating the project root, creating these files, and extracting active governance content into standard owners. This initialization establishes a structured, auditable framework for rules.

Gate-based risk management works by converting hard constraints into measurable checks that validate conformity before deployment. The framework derives these gates from project rules and records evidence to prevent fragile releases.

No, you can apply AI project governance to both new repositories and existing projects requiring improvement. The framework discovers and standardizes existing assets like SOPs or establishes them from scratch.