• Store candidate versions by hash with score, runtime, and external-validation status; the locally highest-scoring version is not always the best submission choice.
• Share reusable optimization idioms while supplying each task only its relevant comments and a compact summary of other feedback.
• Validate unknown candidates through grouped probes, isolate failures, and optimize the final selection under a total runtime constraint.

• 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.

Preserve multiple evaluated candidates and choose a submission under a runtime budget. This write-up shows how task memory and verification can support parallel agent optimization.

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