AI agent memory is the information an agent keeps beyond a single model call: what it has already done in the current task, what it learned about a user last week, which approach worked last time. Language models are stateless. Every call starts from nothing, so anything an agent "remembers" has to be stored somewhere and fed back in.
Memory makes agents more useful, and it also creates questions businesses have to answer: what is stored, where, for how long and who can see it.
Types of agent memory
Short-term (working) memory
The state of the current task: the goal, the steps taken, tool results and the conversation so far. It usually lives in the model's context window. Without it, an agent would repeat its first step forever.
Long-term memory
Information kept across sessions. It comes in three kinds:
- Semantic memory: facts, such as "this customer prefers email" or "the finance approver is Maria".
- Episodic memory: past interactions and their outcomes, such as how a similar ticket was resolved.
- Procedural memory: learned instructions or rules about how to do a task, often stored as updated prompts.
Where memory is stored
- The context window: fast and simple, but limited in size and gone when the session ends.
- A database: structured facts, preferences and task history in tables.
- A vector store: past conversations and documents stored as embeddings and retrieved by similarity.
- Summaries: long histories compressed into short notes to fit the context window.
Memory vs looking it up
This is the design question that matters most for business agents. Much of what people want an agent to "remember", such as a customer's plan, open orders or last ticket, already lives in a system of record. Copying it into agent memory creates a second version that goes stale.
A useful rule:
- Look up anything that lives in a business system and can change: customers, orders, balances, statuses.
- Remember what exists nowhere else: user preferences about how the agent works, notes from past interactions, lessons about what went wrong.
Risks of agent memory
- Stale data: remembered facts that have since changed lead to wrong actions.
- Privacy: personal data stored in memory falls under the same rules as any other personal data, including deletion requests.
- Leakage across users: memory from one user surfacing in another user's session.
- Memory poisoning: false information written into memory, deliberately or by mistake, that shapes future behavior.
Treat agent memory as a data store: scope it per user or team, set retention, and make it inspectable and deletable.
Where Jet Admin fits
Jet Admin agents read live records from the systems you already run, from 200+ integrations including Postgres, Salesforce, Zendesk and REST APIs, so they look up current data instead of relying on remembered copies. Any memory you do want to keep can live in a table you control, under the same role model as everything else. Granular permissions, SSO and audit logs are on the Business plan and above. For the bigger picture, see AI agent architecture.
Frequently asked questions
What is AI agent memory?
The information an agent stores and reuses beyond a single model call, within a task and across sessions.
What is the difference between short-term and long-term memory in agents?
Short-term memory holds the current task's state in the context window. Long-term memory persists facts and history across sessions in a database or vector store.
Do all AI agents need long-term memory?
No. Many business agents work best by looking up current data from systems of record each time.
How do you keep agent memory secure?
Scope it per user, apply the same access rules as other data, set retention limits and make it possible to inspect and delete.
Build agents that read the truth, not a copy
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