How to Preserve Decisions an AI Agent Should Not Reopen

Effective agent memory should preserve decisions as structured, source-linked records rather than relying solely on conversation transcripts, which can blur tentative suggestions and approved outcomes. Useful decision records identify the project, status, date, chosen and rejected options, rationale, source references, uncertainty, and conditions that would trigger review, enabling agents to retrieve relevant constraints without applying them across unrelated work. When decisions conflict with newer records or repository conditions, agents should expose the conflict and distinguish effective dates from ingestion dates. Revisions should create traceable versions while preserving historical rationale, and task-specific exceptions should not silently override general rules. Evaluation should test whether agents respect decisions, correctly scope them to the relevant project, explain their evidence, and adapt to later revisions, while research-derived decisions may require an accompanying evidence ledger and managed-memory pilots should retain the original record as authoritative.

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