Claude + Engineering managers and senior... Playbook

Engineering managers and senior engineers experience constant context-switching due to PR monitoring overhead. Review comments pile up, CI failures go unnoticed for hours, merge conflicts appear silently, and approvals sit waiting. The traditional workflow of checking GitHub, reading comments, fixing issues, pushing, and waiting for CI repeats 20+ times per day.

An AI agent continuously monitors GitHub PRs using a durable polling loop (every 2-15 minutes). When reviewer comments are detected, the agent uses a decision matrix to determine if action is needed: it auto-fixes routine issues, skips dismissals from the author, processes explicit instructions prefixed with 'Jarvis:', and alerts on vague feedback. For fixable comments, the workflow is: parse the comment, checkout the PR branch, apply the fix using AI code understanding, run tests locally, commit and push with a descriptive message, and reply to the comment. For CI failures, the agent fetches logs, identifies the root cause, applies targeted fixes, runs verification, and pushes. All activity is logged and the agent escalates architectural decisions or security-sensitive changes to the human engineer.

Eliminates 15-20 context switches per day for PR maintenance. Comments addressed in under 5 minutes instead of 2-4 hours. CI failures fixed autonomously. Saves 2-3 hours per week on PR maintenance for active contributors.

Polling via: gh pr list --author @me --state open --json number,title,reviewDecision,statusCheckRollup and gh api repos/{owner}/{repo}/pulls/{pr}/comments --jq '.[] | select(.created_at > "[last_check]")'. Author dismissal patterns: 'this is fine', 'intentional', 'keeping as is', 'won't fix', 'by design'. Instruction patterns: commands prefixed with 'Jarvis fix this by...' or 'Jarvis: [instruction]'. Safety guardrails: never force-push, always run tests before pushing, preserve existing code, reply to all processed comments, escalate unclear requests, author replies take priority, never process own comments.

Agent escalates and hands off when: comment requires architectural decision, multiple valid interpretations exist, tests fail after fix attempt, change touches security-sensitive code, or reviewer explicitly requests human review. Relies on durable cron for persistent monitoring across session restarts.