What is AI agent memory and how it works
Davis Christenhuis
AI agent memory is the system that lets an agent retain information from interactions and retrieve it later to inform its responses or actions. It can preserve a user’s preferences, the outcome of an earlier task, or decisions that matter to ongoing work.
Persistent memory typically lives outside the model, where the application saves and retrieves information without retraining it. That continuity can be useful, but it can also carry outdated assumptions into new decisions. Whether an agent should remember depends on its task.
This guide uses a common four-part framework to explain agent memory: working, semantic, episodic, and procedural memory. It also explains how systems store, retrieve, update, and remove information, when persistent memory helps, and how Dust’s Agent Memory tool works.
📌 TL;DR
- AI agent memory lets an agent retain information from interactions and recall it later to inform its responses or actions.
- A common framework distinguishes working memory for active tasks, semantic memory for facts, episodic memory for past events, and procedural memory for methods and instructions.
- Many memory systems extract useful information, store it, retrieve it when relevant, and update or remove what no longer applies. Others retain conversation history or accept explicit memory writes.
- Use persistent memory when earlier decisions matter to ongoing work, and leave it off when unrelated past interactions could bias the result.
- Dust’s Agent Memory tool can be added to custom agents. Saved memories are private to each user, separate for each agent, and available for users to inspect and delete.
What is AI agent memory?
AI agent memory is the information an agent keeps and uses as it works, together with the mechanisms for storing and recalling it. It includes temporary details needed for a current task and longer-term records, such as user preferences, previous decisions, and task outcomes. Persistent memory makes those records available across conversations, so users do not have to supply the same context each time.
The application typically stores persistent memories outside the model and retrieves them when relevant. Saving a preference does not retrain the model. Instead, the application supplies that preference as information the model can use when generating its next response.
Agent memory and retrieval-augmented generation, or RAG, overlap rather than form separate categories. A memory system can use RAG to retrieve saved observations, just as a knowledge-search system retrieves documents.
RAG describes how retrieved information is supplied to a model to support generation; agent memory describes retained information or state that can inform later work.
A RAG index can be updated over time, and a memory system can use RAG for recall, so the distinction is not simply reading versus writing. An agent that searches historical support tickets has access to past records, but that alone does not show that it retains and recalls its own conversations or actions.
Four common types of agent memory
One useful way to organize agent memory is by its function: working memory supports the task in progress, semantic memory preserves facts and concepts, episodic memory records experiences, and procedural memory represents how to carry out work.
The table below compares these categories and their uses.
Type | What it stores | Operator-level example, illustrative | When it matters |
Working memory | Information available during the current task | The current ticket, retrieved policy, and results of a diagnostic check | Coordinating the next action |
Semantic memory | Facts, preferences, and conventions | A user’s preferred reporting format or an agreed metric definition | Reusing relevant knowledge across interactions |
Episodic memory | Specific past events and outcomes | A record of a previous troubleshooting attempt and its result | Understanding what happened before |
Procedural memory | Instructions and methods for carrying out work | An escalation procedure with required approval steps | Following a repeatable process |
The CoALA framework provides the basis for this breakdown: working memory holds active task state, while semantic, episodic, and procedural memory organize longer-term knowledge.
This is a conceptual framework, not a requirement to implement four separate stores. The categories describe memory’s content or function, while short-term and long-term describe its scope and persistence.
1. Working memory
Working memory holds what the agent can use during its current task: the request, relevant instructions, retrieved information, and intermediate results. Other memory types can contribute to it, but working memory is not itself a guarantee of persistence.
In the support-ticket example in the table above, working memory might contain the customer’s current error message alongside the outcome of a diagnostic check. Its purpose is coordination: keeping the information needed for the next decision available without treating every intermediate observation as something worth retaining indefinitely.
2. Semantic memory
Semantic memory stores facts and concepts without requiring the agent to replay the event in which it encountered them. Examples include a user’s preferred report structure, a project’s naming conventions, or a clarified business definition.
The important distinction is between a fact and an inference. “Use fiscal quarters in my reports” is an explicit preference. One request for a fiscal-quarter report is weaker evidence of a standing preference. For a first deployment, explicit, narrowly scoped preferences are a reasonable starting point because your team can inspect whether the agent retained them correctly.
3. Episodic memory
Episodic memory records particular events: what the agent attempted, what happened, and which outcome followed. It preserves the circumstances of an experience rather than reducing everything to a general fact.
An illustrative support memory might record that restarting a service did not resolve a particular error last Tuesday. That could prevent an unnecessary repeat, but only if the agent checks whether the present situation matches. An unsuccessful action in one environment is not proof that the same action will fail everywhere. Dates, conditions, and outcomes make the record useful.
4. Procedural memory
Procedural memory represents how to carry out work. It can include instructions, workflows, and rules for choosing or using tools. In deployed agent systems, teams may express it through prompts, application code, or reusable procedures rather than a separate searchable memory store.
For example, an escalation procedure might require checking the account’s support entitlement and obtaining approval before issuing a credit. Unlike a personal preference, that procedure should not change because one user asks to skip a step. Updates need an owner and a review process appropriate to the consequences.
How agent memory works
An external memory system needs policies for what to retain, where to store it, when to recall it, and when to update or remove it. Extraction, storage, retrieval, and forgetting provide a useful way to explain these responsibilities, but not every implementation performs them as four separate stages.
Some systems retain conversation history, some save selected facts or explicit notes, and others combine these approaches. Processing may happen during a conversation or in the background.
1. Extraction
Extraction turns interactions into candidate memories. The system identifies information that may remain useful, separates it from temporary instructions, and decides whether to create a new record or update an existing one.
This judgment shapes everything downstream. Accurate retrieval alone will not correct a memory that turns “make this response shorter” into “always give this user minimal detail.” Retaining a link to the source interaction can help the system or a reviewer identify and correct that mistake. Storing complete conversation history can preserve useful evidence, but later retrieval still needs to distinguish relevant information from noise.
In an illustrative reporting workflow, “Use the new regional grouping for this month’s board pack” should retain its time and task boundaries. Removing those qualifications changes the instruction. A useful extraction check is whether someone reading only the saved memory would understand where it applies.
2. Storage
The application saves the selected information outside the model so it remains available to future runs. Storage might use structured records, documents, vector indexes, or graph stores, depending on how the application needs to retrieve it.
The storage format matters less to an operator than the information attached to each record. A useful design should identify the relevant user or project, the source interaction, and when the information was recorded or updated. Those details help distinguish a current instruction from an older assumption.
Storage also establishes access boundaries. A private user preference and an approved team procedure may both be useful memories, but they should not automatically have the same audience.
3. Retrieval
Retrieval selects stored information relevant to the current request and makes it available to the agent. Selection may use meaning, exact identifiers, recency, or a combination of these signals. Access checks must constrain what the system can return.
Relevance alone is insufficient. A record about the right account may still describe an expired contract or a decision that someone later reversed. The agent needs enough context to evaluate whether the memory applies now.
Selective retrieval can reduce the amount of historical text sent to the model by supplying relevant records instead of replaying entire conversations. It does not make memory free: extraction, search, storage, and maintenance still add operating costs.
4. Forgetting
Forgetting keeps old information from influencing work after it stops being useful. A growing store can accumulate duplicates, contradictions, and facts that no longer apply, making retrieval and interpretation harder.
Common controls include giving recent information more weight, expiring temporary records, and marking old facts as superseded. These controls are not interchangeable. Lowering a record’s retrieval priority does not delete it, and deleting a record may be inappropriate when an audit requires its history.
The illustrative Frame below follows one reporting instruction through extraction, storage, retrieval, and forgetting.
When your agents need memory, and when they shouldn't
Persistent memory is useful when work spans conversations and earlier decisions still matter. It lets an agent continue from an agreed plan rather than ask users to reconstruct it. For example, an onboarding agent could remember which setup steps an employee has completed and use that record to guide the next session. Those records need to stay current: remembering an unfinished step as complete could cause the agent to skip necessary work.
Leave memory off when previous interactions should not influence the result. For example, an agent scoring grant applications should assess each submission against the approved criteria, not favor an applicant because of a positive earlier exchange. It may still need relevant historical evidence, but that evidence should be deliberately included in the assessment rather than carried over from unrelated conversations.
If you build a memory layer, you also take responsibility for what gets saved, who can access it, and when it is updated or deleted. Using a platform’s built-in memory reduces infrastructure work, but you still need to test whether the agent remembers the right details and stops using outdated ones.
Running agent memory in production: what Dust does
Dust is a multiplayer, multi-model AI platform where people and agents work with company knowledge and connected tools. Its Agent Memory tool lets an agent retain user-specific information across conversations, such as preferences and context about ongoing work.
For a custom agent, open the agent builder, go to Tools, select Memory, and save the configuration. Dust manages the underlying memory infrastructure.
An agent with the Memory tool can decide what information is worth saving, and you can explicitly ask it to remember something. It can use saved information in later conversations and update it as circumstances change. Review important memories rather than assuming that every preference or project detail has been captured correctly.
With this tool, you can inspect and manage your saved memories:
- Private to each user: Sharing an agent does not give other users access to your memory store.
- Separate for each agent: Saved memories are scoped to the individual user and agent.
- View your memories: Click the agent’s name or avatar and open the Memory tab.
- Delete individual items: Remove saved items from the Memory tab.
Saved memories support continuity across conversations, but they should not be treated as an immutable archive. Agents can update or remove remembered information, and users can delete individual items. Review stored context when preferences, responsibilities, or project details change.
Dust encrypts memory data at rest and does not use it for model training. Learn more about its data protection practices on the security page.
In the example below, agent memory is selected in Dust’s agent builder, ready to be added to the agent.
Frequently asked questions (FAQs)
What are the types of AI agent memory?
A common four-part taxonomy comprises working memory for the current task, semantic memory for facts, episodic memory for past events, and procedural memory for instructions. These are functional categories, not a requirement to deploy four separate databases.
What is the difference between a context window and agent memory?
A context window is the token budget available to a model during a call. It generally accounts for supplied context and generated output, although exact input and output limits depend on the model.
Persistent agent memory is information an application saves and makes available to later calls. Applications can also store conversation history, so ending a session does not necessarily mean that its contents disappear.
How do AI agents remember things between sessions?
The application stores selected information and associates it with a user, agent, project, or other scope. During a later interaction, it retrieves relevant records and supplies them to the model. This does not require retraining the model after each conversation.
Do all AI agents need memory?
No. Enable persistent memory when relevant history should influence future work, not simply because the capability exists. An agent handling independent tasks may need only its approved instructions and current inputs, particularly when unrelated prior interactions could distort its judgment.