How to Diagnose Slow CI for AI Coding Agents
Use a recorded GitHub Actions run to separate setup, early feedback, and the full build before changing a slow CI pipeline.
A fast patch still needs an answer from CI and a decision from a reviewer. These articles follow the queues, tests, caches, and release steps between writing code and running it safely.
Use a recorded GitHub Actions run to separate setup, early feedback, and the full build before changing a slow CI pipeline.
A trial-expiry boundary shows how code and generated tests can share the wrong assumption. Write the expected result independently.
Replace fixed sleeps with completion and row-identity assertions, then check a delayed preview and a deliberately wrong result in CI.
Understand GitHub Actions concurrency groups, repeated PR cancellation, and the different queue policy an active deployment needs.
Record PR head, checked-out source, event, and workflow URL together so a valid test-merge result is not confused with stale verification.
Show how a changing GitHub Actions build argument refreshes time-dependent pages while the dependency installation stays reusable.
Use a small explicit dependency graph, a transitive selector, and a required-result gate that rejects failed or missing selected checks.
Separate first-review waiting, review windows, author rework, and dependency waits before asking agents to open more pull requests.
Show a tolerated step failure in the job summary and record the owner, evidence, expiry, and restoration condition of its exception.
A real scheduled deployment failed while unpacking its Docker image. Follow the protected image cleanup, portable-shell repair, and live checks.