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A structured introspection workflow that guides AI agents to review their stored memory entries, surface contradictions, track belief evolution, and generate a self-audit report. Ideal for long-running agents that accumulate context over time and need to stay coherent, updated, and self-aware.
1. Feed memory entries in chronological order so the agent can detect temporal drift accurately — shuffle order only if you want to test contradiction detection in isolation. 2. Run this audit on a schedule (e.g., every 50 interactions or weekly) rather than ad hoc; regular audits catch drift before it compounds into compounding errors. 3. Pipe the 'Recommendations' section back into your memory management system automatically — the DELETE/UPDATE/NEW structure is designed to map directly to CRUD operations on a memory store.
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