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A better home for your agents’ memory.

Separate facts, working context, and durable decisions before adding another vector database.

Research plan. These experiments have not been run; no measured results are claimed.

Memory needs a job description

An agent can need several different kinds of information: temporary task context, durable user preferences, source documents, and records of decisions. Putting everything into one undifferentiated store makes retrieval and correction difficult to reason about.

Design a small memory record

Our proposed record includes the claim, its source, when it was observed, who may use it, and when it should be reviewed. Treat inferred preferences as tentative. A correction should supersede an earlier record rather than silently leaving both active.

  • Facts need provenance and an explicit owner.

  • Decisions need the context in which they were made.

  • Working notes need an expiry or a clear task boundary.

  • Sensitive material needs an intentional retention policy.

Do not confuse caching with remembering

Prompt caching reuses previously processed prompt prefixes when the provider’s cache requirements are met. It is a different mechanism from deciding which facts should persist across tasks. Cache hits can reduce repeated processing costs, while cache-write costs, lifetime, and model support affect the overall saving. Caching does not make the context accurate or appropriate.

Anthropic: prompt caching

Test correction, not just recall

A useful future experiment would ask an agent to retrieve a fact, incorporate a correction, and stop relying on the earlier value. Include cases where the right answer is that the source is missing or out of date. We have not run this evaluation; the record format and test cases are a research proposal.

How this story was made

Prepared with AI assistance. Sources are linked in the story. This is a research plan, not a report of completed experiments. No measured results are claimed.

The conversation

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