Measure cycle time, PR acceptance rate, review effort, rework hours, defect leakage, test coverage, security findings, change failure rate, MTTR, and task success rate. The best metric is not how much code the agent writes, but whether delivery improves without increasing risk.

They can support production software workflows when bounded by human review, automated testing, security checks, and release controls. They should not be trusted as uncontrolled production actors.

Govern AI-generated code through approved tools, access limits, test execution, static analysis, secure coding checks, reviewer approval, CI/CD controls, and traceability from ticket to release.

Start with reviewable tasks such as documentation updates, test generation, bug triage, code explanation, acceptance criteria cleanup, and low-risk refactoring. Avoid starting with production deployment actions or complex architecture changes.

An AI coding assistant helps a developer write or understand code inside the workflow. An AI coding agent can take a task, inspect repository context, plan changes, create outputs, run checks, and prepare a pull request or related SDLC artifact.

No. AI agents can support coding, testing, documentation, review, and release preparation, but enterprise accountability still belongs to human engineering leaders. Developers, architects, QA leads, security teams, and release owners must approve high-risk decisions.