AI BriefingHow to Build Memory for AI Agents
Key point
This covers a methodology for designing memory systems that allow AI agents to learn from past experiences and correct their behavior.
Details
For an AI agent to learn and improve through user feedback or past behavior, implementing Memory is essential. Memory refers to persistent context that an agent can retrieve across executions to guide its behavior.
Memory is broadly divided into two scopes:
- Short-term Memory: Refers to the context of the task currently being performed (recent messages, tool results, intermediate reasoning results, etc.).
- Long-term Memory: Refers to information that persists even after execution ends (facts, preferences, examples, workflows, etc.).
Following classifications from cognitive science, long-term memory can be further broken down into three types:
- Semantic Memory: Facts, preferences, and general knowledge that the agent knows.
- Episodic Memory: Past interactions, actions, and outcomes that the agent has experienced.
- Procedural Memory: Instructions, workflows, and tool-use rules for how the agent should behave.
In particular, Procedural Memory is crucial for correcting an agent's behavioral errors. Issues such as incorrect tool call ordering or ignoring a specific tone can be resolved through procedural memory updates that clarify rules or revise steps.
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