How Software Factories Improve Themselves — Suraj Gupta, Warp
The presentation outlines three architectural patterns for building self-improving software factories: implementing outer-loop agents for skill refinement, utilizing persistent memory stores for traceable facts, and employing model routing to optimize cost and performance. These methods allow automated development environments to continuously improve their capabilities, moving beyond simple agent execution to self-optimization.
Key takeaways
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Outer-Loop Agents for Skill Improvement
6:55
Instead of relying solely on an inner-loop agent to execute a skill (e.g., triage), an outer-loop agent observes the inner agent's runs and human feedback. Improvements to the skill are then formally tracked and reviewed via a Pull Request (PR) in Git, ensuring observability and preventing regressions. (4:15, 5:00)
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Persistent Memory as a Fact Store
8:55
Persistent memory acts as a versioned, traceable fact store scoped to an agent (e.g., a Sentry agent). This allows future runs to leverage previously gathered root cause analysis or context, preventing the waste of tokens and time re-gathering known information. (5:35, 6:35)
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Model Routing for Cost Efficiency
Model routing prevents excessive costs by preventing simple tasks (like triage or minor CI fixes) from running on high-cost models (e.g., Opus). Users can define custom rules or use auto-models to direct tasks to the most efficient model (e.g., using GLM for UI tasks). (8:45, 9:40)