--- title: "Set each stage of the issue pipeline to auto or manual" description: "Choose which stages agents run on their own and keep the others in a person's hands." url: https://starsling.dev/ci-playbooks/techniques/per-stage-automation canonicalUrl: https://starsling.dev/ci-playbooks/techniques/per-stage-automation --- # Set each stage of the issue pipeline to auto or manual Choose which stages agents run on their own and keep the others in a person's hands. ## 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. - **[Mastra](https://starsling.dev/ci-playbooks/mastra#per-stage-automation)**: Use it when Agents open pull requests faster than the team can review them, or a backlog import floods the pipeline. Check before adopting: An incorrect assumption upstream carries into every later stage, so automate the stages with the clearest inputs first and compare several weeks of results before widening. Adapted for GitHub Actions; compare the original system with yours. ## Apply this technique ````text Evaluate and apply this CI technique to my repository: Set each stage of the issue pipeline to auto or manual. Choose which stages agents run on their own and keep the others in a person's hands. Read the technique and its source-backed company examples: https://starsling.dev/ci-playbooks/techniques/per-stage-automation.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 Mastra used this technique Read what Mastra changed and follow the original sources. ### Mastra: Set each stage of the issue pipeline to auto or manual **Translated to GitHub Actions** - What Mastra built: Mastra Factory runs Intake, Triage, Planning, Build, Review and Done on a board, and every step runs in manual or auto mode. - Your GitHub Actions version: Set each stage of your issue pipeline to auto or manual, with triage automatic while planning, implementation and review stay manual. Mastra Factory connects GitHub, Linear and Slack to a board that runs Intake, Triage, Planning, Build, Review and Done, and every step runs in manual or auto mode. Mastra started with full automatic mode, where an issue ran through each stage until review. Importing a backlog that way produced bursts of work, so Mastra made each stage configurable. For example, triage can run automatically while planning, implementation and review remain manual, and bugfixes can flow through automatically while feature requests do not. The two figures Mastra's post gives alongside this setting are Factory-wide: the share of its PRs and issues Factory has handled since July, and the weekly volume of issues and PRs the pipeline works through. Evidence: Company-reported result or observation Since we introduced Factory in alpha in July, it's automated 25-35% of our PRs and closed 50-60% of our issues. ([Source](https://mastra.ai/blog/announcing-mastra-factory-beta)) We see over 100 opened issues per week, and merge around 200-250 PRs. ([Source](https://mastra.ai/blog/announcing-mastra-factory-beta)) Use it when: Agents open pull requests faster than the team can review them, or a backlog import floods the pipeline. Before applying it: An incorrect assumption upstream carries into every later stage, so automate the stages with the clearest inputs first and compare several weeks of results before widening. GitHub Actions adaptation: Compare the source system with your own workflow before applying this translation. [Read more in the Mastra playbook](https://starsling.dev/ci-playbooks/mastra#per-stage-automation) [Primary source](https://mastra.ai/blog/announcing-mastra-factory-beta) ## 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