AI agent memory can be built in layers. It can combine a summary of the current context with a long-term knowledge base that the agent searches for relevant facts when you ask a question
ChatGPT can also save details and use past conversations when those features are available and enabled. As OpenAI explains, its memory does not retain every detail. In your own system, you can configure what gets saved, how it is searched and which agents can see it
I have built a multi-layer memory system. Each agent has a rotation schedule: every day or week, depending on how often it is used, the system selects what matters and creates a summary. The rest goes into an archive to free up the agent's working memory
What should an agent remember about a client?
The working context holds the current request and details of the conversation. As a dialogue gets longer, the system can keep a summary and leave the full conversation in an archive. Summaries save space, but an important detail can get lost, so it helps to keep a link to the original conversation
For ongoing work, separate records can hold what the client needs, what was agreed, the decisions made and what has changed. Give each record a source and a date. If the client changes the terms, the agent can see the current agreement instead of confidently quoting an old one
How does memory find the right information?
A separate model can process conversations and organize facts, decisions and conclusions in a knowledge base. PostgreSQL stores records, while search helps find them. For example, pgvector adds similarity search to PostgreSQL and can be combined with keyword search. The database does not analyze conversations by itself - that takes separate logic
When a new question arrives, the system searches for relevant records and adds them to the agent's context. If a client asks about an earlier agreement, the agent can receive the fact it found along with its source. Search can still miss a record or return an outdated one, so important decisions should be checked against the original
Where to start
Decide what the agent actually needs to know and who can access it. Not every conversation belongs in shared memory: data from different clients needs to be kept separate, and incorrect records need a way to be corrected
Memory deserves attention from the start. Over time, the data you collect forms a knowledge base that can become your second digital consciousness. I explain how to connect memory with tasks and other components on my AI systems architecture page