Governing Agentic AI At Scale: Securing AI-Generated Code In The CI/CD Pipeline guide for production teams: compare workflow fit, risk, cost, review burden.
Key takeaways
- AI-generated code now belongs inside normal software delivery governance, with extra evidence around agent identity, prompt context, artifact provenance, and approval history.
- CI/CD is the right control plane because it already decides what code can build, test, package, deploy, and roll back.
- Human review should be risk-based. Low-risk agent changes can pass through policy checks, while dependency, credential, infrastructure, and production-release changes should escalate.
- DORA-style metrics still matter, but they are incomplete when dashboards cannot distinguish human-authored changes from autonomous agent activity.
- The practical operating model is provenance, signing, policy gates, isolated execution, continuous monitoring, and clear rollback ownership.
📖 Read the full guide on Van Data Team → Governing Agentic AI at Scale: Securing AI-Generated Code in the CI/CD Pipeline
Top comments (0)