Property Testing with Agent Swarms

7.7 relevance

Directly relevant to AI agent testing and multi-agent systems, highly actionable.

AI/ML recursion.wtf

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Why It Matters

For a platform engineer debugging complex data structures or query planners, this approach offloads the tedious work of writing property tests to agent swarms, turning a one-time token investment into a reusable, zero-cost-per-run bug-finding pipeline.

Key Takeaways

Clone the agent-skills repo, point a planning agent at your most complex internal data structures, and let it generate reference implementations and proptest suites to surface correctness bugs automatically.

Summary

A property-testing agent skill enables swarms to find correctness bugs in complex repos by generating reference implementations and assertions, then running proptest-based checks without per-case token costs. Applied to Codex, it found 13 distinct bugs (including a rollback that erases preserved review history and a plan replacement bug), with 12 proposed fixes. The same recipe on uv, Prometheus, and Babel yielded merged fixes for optional-dependency activation, BucketQuantile panics, and histogram metadata loss, while a third-party user found two IndexedDB bugs in KittyCAD.

Author

Inanna Malick