--- title: "Write every failure to a file and let fixer agents work through it by area" description: "Failing tests and compiler errors are saved to a file, grouped by package, and each group goes to an implementer, reviewers and a fixer." url: https://starsling.dev/ci-playbooks/techniques/failures-as-work-queue canonicalUrl: https://starsling.dev/ci-playbooks/techniques/failures-as-work-queue --- # Write every failure to a file and let fixer agents work through it by area Failing tests and compiler errors are saved to a file, grouped by package, and each group goes to an implementer, reviewers and a fixer. ## How to choose an approach This technique currently has one documented company example. Validate its prerequisites, tradeoffs and original CI environment against your repository before adapting it. - **[Anthropic](https://starsling.dev/ci-playbooks/anthropic#failures-as-work-queue)**: Use it when A change leaves hundreds of failing tests or errors, or an agent fixes failures from a log too large to read at once. Check before adopting: Run the failing check once at the start of each group and commit at the end, so parallel agents do not start overlapping builds, and have a person read the loop's output and edit its instructions when it drifts. Adapted for GitHub Actions; compare the original system with yours. ## Apply this technique ````text Evaluate and apply this CI technique to my repository: Write every failure to a file and let fixer agents work through it by area. Failing tests and compiler errors are saved to a file, grouped by package, and each group goes to an implementer, reviewers and a fixer. Read the technique and its source-backed company examples: https://starsling.dev/ci-playbooks/techniques/failures-as-work-queue.md Inspect this repository’s CI workflows, build and test commands, dependencies, and recent run timings. Identify where this technique applies and explain the expected benefit using this repository’s evidence. Measure a baseline from recent comparable runs: elapsed time, queue time, runner minutes and failures where available. If data is unavailable, say what cannot be measured. Compare relevant company approaches, checking prerequisites, platform differences and tradeoffs. Rank applicable options by evidence, fit and measurable opportunity; do not present company results as predictions for this repo. Recommend the smallest safe change, show what checks and fallback paths remain, and ASK ME WHICH CHANGE TO APPROVE BEFORE EDITING ANY FILES. Only after approval, work on a new feature branch, make one reversible change, and open a reviewable pull request. Never merge or push to a default or protected branch. Keep required checks, test coverage, deployment and release behavior intact. Validate the change and compare equivalent runs where possible; report limitations and what should be monitored after merge. ```` ## How Anthropic used this technique Read what Anthropic changed and follow the original sources. ### Anthropic: Write every failure to a file and let fixer agents work through it by area **Translated to GitHub Actions** - What Anthropic built: Bun's Rust port wrote about 16,000 compiler errors to a file, grouped by crate, and a workflow divided them among 64 Claudes. - Your GitHub Actions version: A workflow step that writes every failure to a file, grouped by area, for fixer agents to work through. Once Bun's Rust port was written, cargo check wrote about 16,000 compiler errors to a file, grouped by crate, and a workflow divided them among 64 Claudes: 16 loops across 4 worktrees, each with one Claude fixing, two reviewing and one applying. A second workflow looped on test files: each failing test saved its stack trace and errors to a file, one implementer proposed a fix, two adversarial reviewers checked it and a fixer applied it. The test runner gained a --results-json option that writes per-file results as JSON for those test-fix workflows. Evidence: Company-reported result or observation Two days after the first CI run, the failing list was down from 972 test files to 23. ([Source](https://bun.sh/blog/bun-in-rust)) Use it when: A change leaves hundreds of failing tests or errors, or an agent fixes failures from a log too large to read at once. Before applying it: Run the failing check once at the start of each group and commit at the end, so parallel agents do not start overlapping builds, and have a person read the loop's output and edit its instructions when it drifts. GitHub Actions adaptation: Compare the source system with your own workflow before applying this translation. [Read more in the Anthropic playbook](https://starsling.dev/ci-playbooks/anthropic#failures-as-work-queue) [Primary source](https://bun.sh/blog/bun-in-rust) ## CI optimization is continuous work. Apply this technique yourself, or use StarSling Runners for faster GitHub Actions and optional agent-driven optimization PRs, which teams on eligible paid plans can turn on. [Get self-improving CI](https://starsling.dev/products/runners) ## Company Playbooks ### [Anthropic](https://starsling.dev/ci-playbooks/anthropic) Run only affected tests ### [DoorDash](https://starsling.dev/ci-playbooks/doordash) Run build and test in parallel, sharded across CI agents ### [Linear](https://starsling.dev/ci-playbooks/linear) Benchmark the same job on faster hardware ### [Mastra](https://starsling.dev/ci-playbooks/mastra) StarSling runners for the slowest test workflow ### [OpenAI](https://starsling.dev/ci-playbooks/openai) Rank CI fixes by the minutes they save ### [Ramp](https://starsling.dev/ci-playbooks/ramp) Move a CPU-bound test suite to a faster CPU ### [Shopify](https://starsling.dev/ci-playbooks/shopify) Let an agent iterate against one CI metric ### [Spotify](https://starsling.dev/ci-playbooks/spotify) Build arm64 on native arm64 runners ### [Stripe](https://starsling.dev/ci-playbooks/stripe) Run only the tests a change can reach ### [Uber](https://starsling.dev/ci-playbooks/uber) Build the merge-queue speculations most likely to be needed