Workflow playbook
AI coding agent review loop workflow
Use coding agents for scoped repository changes while preserving task boundaries, command evidence, product review, and rollback readiness.
Target users
- Product engineers
- Solo founders
- AI builders
Inputs
- Task brief
- Repo context
- Verification commands
- Risk notes
Outputs
- Reviewed diff
- Verification evidence
- Rollback notes
Boundaries
- Do not let agents make irreversible data or production changes without explicit review.
- Keep generated changes small enough for a human to understand.
- Require command output before treating the implementation as done.
Common mistakes
- Giving an agent a vague product goal instead of a small implementation task.
- Accepting broad refactors because tests still pass.
- Skipping security, data, and rollback review for generated code.
Templates
- AI coding agent task brief
- Agent diff review checklist
Primary tools
Alternatives
Steps
- 1
Define the agent task
Write the narrow behavior change, files to inspect, files to avoid, and acceptance criteria.
Output: Scoped agent task brief.
- 2
Generate and inspect the diff
Let the coding agent implement the task, then review scope, patterns, and unintended changes.
Output: Reviewable repository diff.
- 3
Verify and hand off
Run typecheck, build, tests, and smoke checks, then document risk and rollback path.
Output: Verification handoff notes.
Copyable prompts
Convert this product issue into an AI coding agent task with scope, non-goals, files to inspect, and verification commands.
Review this diff for scope drift, missing tests, security issues, and rollback risk.
Related tools
Related guides
Use cases
- Agent-coded feature
- Bug fix review
- Prototype hardening