An AI agent should remember only information that is likely to improve future work, is appropriate to retain, and can be kept accurate and under the right access controls. That usually means explicit user preferences, durable project decisions and their rationale, and lessons that prevent repeated effort—not a permanent transcript of every conversation.
What should an AI agent actually remember?
Separate information into three places according to its job:
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- Session context: conversation details and working material needed to complete the current task. Keep these available for the session; they do not automatically merit long-term storage.
- Persistent memory: a concise, curated record of durable facts that may help in later interactions, such as a user’s explicit preference or a project decision.
- Knowledge sources and tools: authoritative material that changes or needs maintenance, such as policies, runbooks, documentation, and code. Keep it in a source that can be updated and governed independently, then retrieve it when needed.
Microsoft’s multi-agent reference architecture puts the distinction plainly: “If the workflow already exists as documentation, a runbook, or code, it belongs in a knowledge source or in a tool, not in memory.” (Microsoft, “Memory,” last updated August 4, 2026.)
How do you decide what an agent should remember?
Evaluate each candidate item before making it persistent. A useful memory is durable, relevant to future work, trustworthy enough to rely on, correctly scoped, and safe to retain.
#1 Best Overall
- Will it matter later? Favor explicit requests to remember something, recurring preferences, consequential project decisions, and outcomes that prevent the agent from repeating unproductive work. An incidental detail from one exchange is weaker evidence of future usefulness.
- Is memory the right home? Keep task-specific details in session context. Put authoritative or frequently changing material in a maintained source of truth. Use long-term memory for a small set of curated statements, not a duplicate document store.
- Can the agent tell what the information applies to? Mark whether a fact concerns a particular user, project, team, organization, or agent. Preserve enough context to avoid turning a one-time request into a universal preference, and decide which agents are allowed to retrieve it.
- Is retaining it appropriate? Consider sensitivity, user expectations, access, the applicable retention policy, and whether the user can inspect, correct, or delete it. If the system cannot govern a fact appropriately, do not promote it to persistent memory.
- Will it be retrieved only when relevant? Decide whether a compact profile should be available by default or whether records should be retrieved on demand. A stored fact can still be harmful or distracting if it is injected into unrelated tasks.
A practical record might include the memory statement, its subject and scope, source or context, time recorded, importance, and lifecycle policy. This is an implementation recommendation, not a schema required by the cited architecture.
What belongs in persistent memory?
Explicit preferences and constraints
Remember a preference when the user has clearly asked for it to carry forward or has established it consistently. Keep the wording specific enough to apply correctly: for example, a preference about response format should not silently become a preference about every project or audience.
Project decisions and rationale
A durable decision can save future work when the agent needs to continue a project. Store the decision with its scope and, when it matters, why it was made. If the decision changes, update or retire the old memory rather than letting conflicting versions accumulate.
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Retain concise lessons that help avoid repeated exploration or account for a user correction. OpenAI’s Agents SDK describes memory as a way for future sandbox-agent runs to learn from prior runs, distinct from conversational Session history; its examples present reduced repeated exploration, user corrections, and context recovery as intended uses, not independently measured guarantees. See OpenAI Agents SDK: “Agent memory”.
Rank #3
What should an agent not remember?
- Every conversation verbatim: a transcript is not a curated memory. Retain only details with a clear future purpose and an appropriate basis for keeping them.
- Temporary task state as permanent fact: drafts, one-off instructions, and intermediate details often belong only to the current session.
- Changing reference material as if it were timeless: policies, procedures, and documentation can become stale. Keep them in maintained sources and retrieve the current version.
- Unscoped or ambiguous statements: a fact without a clear subject, project, or time context can be applied too broadly.
- Information the user cannot govern: persistent storage should have appropriate visibility, correction, deletion, and retention controls.
Microsoft’s architecture cautions that memory is “one of the riskiest parts of the architecture, because everything an agent remembers is data that must be scoped, governed, secured, and eventually forgotten.” (Microsoft, “Memory,” last updated August 4, 2026.)
Which memory approach fits an agent?
There is no universally best architecture. Choose based on what needs to persist, how it should be retrieved, who owns it, and how it will be governed.
| Approach | Best suited to | Main consideration |
|---|---|---|
| Current-session history | Context needed to complete the task underway | It supports continuity in the current interaction; it is not, by itself, a curated long-term record. |
| Compact structured profile | A small number of durable user or project facts that are useful across tasks | Decide what is included by default, how it is scoped, and how a user can review or change it. |
| Episodic records retrieved on demand | Prior interactions or lessons that may matter for a particular later task | Retrieval should select relevant records rather than inject everything into every interaction. |
| Maintained knowledge source or tool | Runbooks, policies, documentation, code, or other authoritative material | Keep the material current and manage updates and access at its source. |
These are design patterns, not a performance ranking. Microsoft’s memory architecture patterns and long-term memory guidance discuss choices around storage, retrieval, and scope; the documentation does not establish one approach as universally faster or more effective.
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How should persistent memory be governed?
Memory is not just a storage decision. Define its ownership, access, and lifecycle when designing the system:
Best Value
- Ownership and scope: specify whether a record belongs to a user, project, team, organization, or agent, and which agents may use it.
- Visibility and correction: give users suitable ways to see what is retained and fix information that is wrong or out of date.
- Deletion and expiry: set how memory is reviewed, retired, or deleted in line with the applicable retention policy. Support temporary use when persistent retention is not wanted.
- Retrieval discipline: retrieve records for a relevant task rather than treating every stored fact as globally applicable.
These controls help keep persistent memory useful without letting it become an uncontrolled archive. The exact implementation depends on the application and its governing requirements.
Is there an ideal number of memories or retention period?
No universal number of facts or retention period is established by the cited architecture and product documentation. Set a policy for the specific application, then evaluate whether stored information remains useful, accurate, appropriately scoped, and governable. Avoid treating an isolated vendor target or product example as a general rule.
What do managed memory services provide?
Managed services are one possible implementation, not a requirement for every agent. AWS describes Amazon Bedrock AgentCore Memory as APIs for storing and retrieving short-term and long-term memory. Its documentation distinguishes immediate conversational context from persistent information; check the current service documentation and terms when assessing whether it fits a particular system. See Amazon Bedrock AgentCore: “How it works”.
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