How Software Factories Improve Themselves — Suraj Gupta, Warp
Source
AI Engineer
Author
AI Engineer
Date
Key takeaways · AI-distilled
In Warp's setup, an outer-loop AI agentAn AI system that doesn't just answer once but works toward a goal in a loop — taking actions, reading the results, and deciding what to do next.Full definition → watches the triage agent's runs and feedback, then opens a pull request to update its agent skillA reusable instruction file that teaches an agent how to do one job well — the procedure, the tools, and what counts as done.Full definition →, so every skill change is tracked in Git and reviewed by a human.
Warp's persistent memory is a versioned, traceable store of facts that lets a Sentry agent reuse a root cause it already found instead of rediscovering it, and it works across harnesses including Claude Code and Codex.
model routingSending each request to a model chosen by the difficulty of the task, rather than using one model for everything.Full definition → keeps teams from paying Opus prices for triage or simple CI fixes; they can use Warp's auto models or write their own routing rules.
Gupta says Warp's internal evals found UI tasks run well on GLM, and routing evals are the next step for the feature.
Terms in this piece · Glossary
model routing — Sending each request to a model chosen by the difficulty of the task, rather than using one model for everything.
agent harness — The scaffolding around a model that turns it into a working agent — the loop, the tools it can call, and the rules for when to stop.
AI agent — An AI system that doesn't just answer once but works toward a goal in a loop — taking actions, reading the results, and deciding what to do next.
agent skill — A reusable instruction file that teaches an agent how to do one job well — the procedure, the tools, and what counts as done.
Why it matters
Shows working mechanisms for a factory to improve itself: skills updated via reviewed PRs, persistent cross-agent harnessThe scaffolding around a model that turns it into a working agent — the loop, the tools it can call, and the rules for when to stop.Full definition → memory so agents don't re-solve known bugs, and evals-driven model routing to cut cost without losing quality.
