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An AI agent should remember durable information that will improve future work—such as an explicit user preference, a project decision and its rationale, or a lesson that prevents repeated effort. It should not preserve every conversation by default. Keep temporary details in the current session, authoritative reference material in maintained documents or tools, and only carefully selected information in persistent memory.
What should an AI agent remember?
Remember something when it is likely to matter again, belongs in memory rather than another source, and can be retained with appropriate scope and controls. Good candidates include:
- Explicit preferences and constraints: for example, a user’s requested writing style or a recurring accessibility requirement.
- Project decisions and rationale: choices that future work should respect, along with enough context to avoid misapplying them.
- Useful outcomes and corrections: lessons from prior work that can prevent repeated exploration or help an agent recover relevant context.
These are categories, not a universal checklist or quota. A detail’s importance depends on the application and who will use it. Microsoft’s multi-agent reference architecture describes memory as information that must be scoped, governed, secured, and eventually forgotten.
What belongs in session memory, persistent memory, or a knowledge source?
These serve different purposes. Session history supports the current task; persistent memory is a curated record that can inform later interactions; a knowledge source or tool holds maintained reference material.
#1 Best Overall
| Information | Best fit | Why |
|---|---|---|
| Temporary details needed to complete the current task | Session context | They may become irrelevant when the task ends. |
| Durable user preferences or project decisions likely to matter in later work | Persistent memory | A concise, scoped record can help future interactions. |
| Runbooks, policies, procedures, or other authoritative material that changes | Maintained documentation or a tool | It can be updated and governed at its source rather than copied into a memory that may go stale. |
Microsoft’s guidance puts existing documentation, runbooks, and code in a knowledge source or tool rather than memory. This distinction matters: remembering a pointer or relevant fact is not the same as making a memory store the authoritative copy.
How do you decide what an agent should remember?
Use these questions for each candidate, before promoting it to persistent memory:
- Will it matter later? An explicit request, recurring preference, consequential project decision, or useful lesson has a stronger case than an incidental conversational detail.
- Is memory the right place? Keep task-local context in the session and changing reference information in its maintained source. Reserve persistent memory for curated statements that help with later work.
- Is it trustworthy and properly scoped? Retain enough context to avoid treating a one-off statement as a universal preference. Decide whether the information belongs to a user, project, team, organization, or a particular agent—and which agents may retrieve it.
- Is it appropriate to retain? Consider sensitivity, user expectations, access, correction, deletion, and the applicable retention policy.
- Should it be retrieved now? A stored fact is not automatically relevant to every task. Retrieve it when it applies rather than injecting every remembered detail into every interaction.
For an implementation, a candidate record could include the memory statement, its subject and scope, source or context, time recorded, importance, and lifecycle policy. That is a practical design suggestion, not a schema required by the architecture.
How should persistent memory be governed?
Memory is not just a storage choice. A persistent record can be useful in one context and inappropriate or misleading in another. Define its owner and permitted audience, and provide controls that let users inspect and correct what is retained, use temporary or non-persistent interaction where appropriate, and delete information under the applicable policy.
Rank #3
Set a lifecycle for each memory category instead of assuming that everything should remain indefinitely. The reviewed architecture guidance does not establish one universal retention period, fact limit, or ideal memory size; those choices must be evaluated for the application.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Which memory approach should an agent use?
No single architecture is best for every agent. Compare approaches based on the information they hold and how the application manages it:
- Durability and content: session history, a compact structured profile, or episodic records of earlier interactions solve different needs.
- Retrieval: an application might supply a small profile by default or retrieve relevant records on demand.
- Authority and freshness: durable preferences and project facts may suit memory; changing policies and procedures usually need an independently maintained source.
- Scope and ownership: determine whether a record belongs to a user, project, team, organization, or agent boundary.
- Governance: consider visibility, correction, deletion, temporary-use controls, and retention rules as part of the design.
For example, the OpenAI Agents SDK describes memory as distilled lessons from prior runs, separate from conversational Session history. Its documentation presents reduced repeated exploration, user corrections, and context recovery as intended use cases, not independently measured guarantees: Agent memory. AWS documents APIs for storing and retrieving short- and long-term memory in Amazon Bedrock AgentCore; this is one possible implementation category, not a requirement for every agent: How it works — Amazon Bedrock AgentCore.
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Fix the driver behind crashes, sound loss and screen glitchesFind Drivers →Repair Windows errors before they cause bigger problemsFix Now →Scan for outdated or missing drivers - takes under a minuteDriver Scan →These examples illustrate product approaches, not a performance ranking. The architecture guidance describes different storage and retrieval patterns but does not establish comparative performance claims.
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