--- title: "Size the agent pool to the widest layer of the build graph" description: "Allocate CI agents per commit from the build graph's widest layer, in place of a fixed count." url: https://starsling.dev/ci-playbooks/techniques/size-agents-to-graph-width canonicalUrl: https://starsling.dev/ci-playbooks/techniques/size-agents-to-graph-width --- # Size the agent pool to the widest layer of the build graph Allocate CI agents per commit from the build graph's widest layer, in place of a fixed count. ## 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. - **[DoorDash](https://starsling.dev/ci-playbooks/doordash#size-agents-to-graph-width)**: Use it when Every commit gets the same number of parallel jobs or runners, whatever the change's build graph needs. Check before adopting: A graph's width approximates how many runners stay busy; long single tasks still set the wall time, so check the critical path as well. Adapted for GitHub Actions; compare the original system with yours. ## Apply this technique ````text Evaluate and apply this CI technique to my repository: Size the agent pool to the widest layer of the build graph. Allocate CI agents per commit from the build graph's widest layer, in place of a fixed count. Read the technique and its source-backed company examples: https://starsling.dev/ci-playbooks/techniques/size-agents-to-graph-width.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 DoorDash used this technique Read what DoorDash changed and follow the original sources. ### DoorDash: Size the agent pool to the widest layer of the build graph **Translated to GitHub Actions** - What DoorDash built: DoorDash walks its operation graph layer by layer and allocates CI agents to the widest layer. - Your GitHub Actions version: Size the number of parallel jobs to the widest layer of the build graph. DoorDash first ran a fixed ten agents per commit. It now walks the operation graph layer by layer, folding no-op operations into their consumers, and takes the widest layer (adjusted by weight) as the number of operations that can run in parallel. Together with the post's runtime improvements, allocating agents to that width cut its largest web project from 220 agent minutes per run to 30. DoorDash's Kotlin build service also scales its workers with the queue: more during U.S. working hours, and more when jobs wait for a machine with enough memory tokens. Evidence: Company-reported result or observation For CWN, this results in a drop from 220 minutes per run to 30 minutes per run ([Source](https://careersatdoordash.com/blog/how-doordash-slashed-web-developer-build-times/)) Use it when: Every commit gets the same number of parallel jobs or runners, whatever the change's build graph needs. Before applying it: A graph's width approximates how many runners stay busy; long single tasks still set the wall time, so check the critical path as well. GitHub Actions adaptation: Compare the source system with your own workflow before applying this translation. [Read more in the DoorDash playbook](https://starsling.dev/ci-playbooks/doordash#size-agents-to-graph-width) [Primary source](https://careersatdoordash.com/blog/how-doordash-slashed-web-developer-build-times/) ## 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