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AI agent memory is the set of mechanisms an agent uses to keep and retrieve information across interactions. It is not necessarily one database: a practical design separates short-term session state from selected long-term records, then assembles only relevant information into the working context the model sees for a particular call.
What is AI agent memory?
AI agent memory lets an agent carry useful information forward instead of treating every interaction as isolated. The AWS Well-Architected Agentic AI Lens defines it as the mechanisms by which agents store and retrieve information across interactions.
That definition describes a system function, not a particular storage product. A memory system may include rules for capturing information, one or more stores, retrieval logic, and controls over what is supplied to a model. Storing a record does not mean the model automatically sees it.
How do the main memory types differ?
Two useful distinctions answer different questions: how long information is retained, and what kind of information it represents. Short-term, long-term, and working memory describe scope or use in an interaction; semantic, episodic, and procedural memory describe the content.
| Type | What it holds | Example | Design implication |
|---|---|---|---|
| Short-term (session) memory | Recent state for one conversation or task | Recent turns, tool outputs, active task variables | Manage the session lifecycle and context limits; production services may need state stored outside an individual process. |
| Long-term (persistent) memory | Selected information retained across sessions | A stable user preference or a prior outcome | Define how information is extracted, consolidated, retrieved, owned, retained, and deleted. |
| Working memory | The context assembled for the current model call | Instructions plus relevant session details and retrieved persistent records | Treat it as context assembly, not necessarily as a durable store. |
| Semantic memory | Facts and attributes | A preference or an account attribute | Compact structured profiles or documents can suit stable facts; retrieve authoritative, changing domain information separately. |
| Episodic memory | Timestamped events and interaction history | A prior support interaction or decision | Retrieve relevant events on demand using suitable metadata and relevance filters. |
| Procedural memory | Methods, workflows, and learned patterns | A method inferred from repeated outcomes | When an approved procedure already exists in a runbook, documentation, or code, use that authoritative source rather than duplicating it as agent memory. |
These categories can overlap. A decision, for example, can be an episodic record of what happened and also yield a semantic fact that should persist. They are design distinctions, not a single settled taxonomy. A 2025 survey, Memory in the Age of AI Agents, describes additional lenses: memory forms (token-level, parametric, and latent), functions (factual, experiential, and working), and dynamics (how memory is formed, changed, and retrieved). It also notes that terminology and evaluation protocols vary across the literature.
What does the model actually see?
The model sees the working context assembled for a given inference, not necessarily every record in every memory store. The Microsoft multi-agent reference architecture, last updated August 4, 2026, describes working memory as the information presented to the model and short-term and long-term memory as design choices about what to include and at what cost.
That distinction matters because stored information has to be selected and placed into a request. A system might include a compact profile by default, retrieve a specific past event only when relevant, or combine both approaches. The available context budget, retrieval latency, permissions, and risk of missing a useful detail all affect that choice.
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- Capture the active state. Keep the conversation turns, tool results, and task variables needed for the current session. For development, state can live in process memory; for scalable production services, Google Cloud’s architecture guidance describes externalizing it so service instances can retrieve and update state per request.
- Select what should persist. Extract information likely to help in future interactions, such as a durable preference, a relevant decision, or a useful prior outcome. A full transcript is not automatically a useful long-term memory.
- Consolidate and resolve. Merge duplicates, update stale records, and apply explicit rules when new information conflicts with an existing record. Microsoft’s Microsoft Foundry Agent Service memory documentation describes extraction, consolidation, and retrieval for its managed memory feature, which the documentation labels as preview.
- Store according to content and scope. Select a representation that suits the information and how it will be found. Microsoft’s memory architecture patterns describe structured relational or document profiles as a common fit for semantic facts, and vector indexing for episodic recall. A graph store is useful when following relationships is important enough to justify it.
- Retrieve into working context. Select records relevant to the task, then check that the requester is allowed to access them and that they fit the context budget. Do not inject every stored record by default.
- Maintain the lifecycle. Make it possible to correct, expire, or delete records, and define how scope is enforced so information from one project, user, or tenant does not silently appear in another.
How should session state be stored?
In-process memory is straightforward for a development system, but it is tied to the lifetime and location of that process. If a request reaches another instance, or the original process restarts, the state may not be available there. Externalizing session state lets instances retrieve and update it across requests, supporting production scalability and reliability. Google Cloud’s guidance names Memorystore for Redis and Firestore as examples, and notes a relational database option for the cited ADK service; these are implementation examples, not requirements.
How is agent memory different from a knowledge base or RAG?
Memory is usually information about a particular user, interaction, or collaboration that would otherwise be lost. A knowledge base, enterprise search index, or retrieval-augmented generation (RAG) corpus holds shared content that remains authoritative independently of a particular conversation and may change over time.
Keep shared source material in its authoritative system and retrieve it when needed, checking permissions at retrieval time. Copying it into personal agent memory can create stale duplicates and blur who is allowed to see it. A vector index can support episodic memory or document retrieval; the storage technology alone does not determine which role it serves. The 2025 survey treats memory, RAG, and context engineering as related but distinct concepts.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Which architecture choices matter most?
- Push or pull: Including a small, stable profile in each relevant request can reduce the chance of missing it, but costs context space. Retrieving records only when relevant can reduce unnecessary context, but depends on retrieval quality and adds retrieval work.
- Representation: Use structured records for stable facts when their fields and updates are predictable; searchable, indexed event histories for episodic recall; and procedural memory for methods the agent has actually learned rather than procedures already maintained in authoritative documentation.
- Scope and access: Decide whether a record belongs to a session, user, project, or shared workspace. Apply permission checks during retrieval and isolate tenants and channels where their data must remain separate.
- Lifecycle rules: Establish what qualifies for saving, how duplicates and conflicts are handled, when records expire or decay, whether users can review them, and how deletion works.
- Operational quality: Evaluate whether retrieval returns relevant records without excessive unrelated material, how token use and retrieval-plus-inference latency affect the task, and whether users still have to repeat information the agent should have retained.
There is no universally best design: workloads differ, and the literature does not yet use one consistent definition or evaluation protocol. Microsoft Foundry’s managed memory feature is documented as a preview, so its availability and behavior may change; check the current documentation before relying on it.
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