evolving-cognition

To improve agent prompts using measurable outcomes, you capture task results, distill causal lessons via Analyzer prompts, and store them in a cognition store for retrieval in future prompts.

A cognition store schema for agent memory structures distilled lessons with retrieval-ready fields like id, created_at, lesson, causal_factor, applies_when, and confidence to enable future prompt refinement.

You build a feedback loop for autonomous agents by defining fitness signals, capturing outcomes, distilling them into lessons, and feeding those lessons back into future prompts using UCB1 retrieval.

UCB1 retrieval ranks past lessons for agent prompts by balancing exploitation of high-confidence lessons with exploration of less frequently retrieved entries to optimize future task performance.

Yes, you need a defined outcome schema to use evolving cognition patterns. The system requires measurable fitness signals, an Analyzer prompt, and a retrieval mechanism with guardrails to ensure evidence integrity.

To handle contradictions and stale lessons in an agent memory store, you run audit loops and poison mitigations that flag conflicting entries and retire outdated lessons to maintain safety and consistency.