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The author discusses the challenge of verifying the correctness of AI agents' long-term memory and asks for community experiences on tracking and correcting memory errors.
The paper introduces PlanFence, a dependency-scoped validation protocol that prevents stale-plan execution in distributed LLM-agent systems by validating plans against current public records, demonstrated through controlled live workflows.