Useful agent memory is a loop. The agent writes what should survive the current session, then retrieves the relevant pieces before the next important response.
Chat history is the transcript of what happened. Memory is the durable set of facts, preferences, corrections, decisions, and procedures that should influence future behavior.
If you store every message as memory, retrieval gets noisy. If you store nothing, every session starts from zero. The right system extracts what is worth carrying forward.
Write after onboarding, explicit preferences, corrections, decisions, recurring behavior, or completed tasks. These are the moments most likely to matter later.
The write path should preserve evidence and scope. A user preference should not become an organization-wide policy, and a temporary task should not live forever as a permanent fact.
Before generating an answer, ask the memory layer for context related to the current query. Retrieval should combine semantic similarity, keyword matches, temporal logic, and source provenance.
The model should receive compact context with enough metadata to know what is current, what is historical, and where each claim came from.
The hardest part of memory is not the first write. It is what happens when reality changes. Users change preferences. Companies reverse decisions. Projects move deadlines.
A production memory layer needs supersession, inactive memories, temporal reasoning, and the ability to answer as of a date or with the latest known fact.
Often, yes. The extractor can use the full conversation to decide what is worth remembering, but only verified useful claims should become durable memory.
Yes, if the assistant response contains a decision, summary, plan, correction, or user-approved fact that should persist. Store the source and evidence so the memory remains auditable.
Use RetainDB to write durable memory and retrieve the right context before every important agent response.
These guides reinforce the memory, context, and benchmark cluster this article belongs to.