Agent Memory

What is Agent Memory?

Agent memory is the system that enables AI agents to persist, retrieve, and reason over information across conversation turns and sessions, providing continuity beyond the immediate context window. It bridges the gap between the model's fixed context window and the unbounded information an agent accumulates over time.

Agent memory architectures typically implement three tiers. Working memory holds the current task state — active goals, intermediate results, and pending actions — within the context window. Short-term memory stores recent conversation history with summarization when it exceeds budget. Long-term memory persists facts, preferences, and learned patterns across sessions using external storage like vector databases or structured knowledge graphs.

The retrieval mechanism determines memory effectiveness. Naive approaches stuff all available memory into the context, wasting tokens on irrelevant information. Production systems use semantic search to retrieve only memory relevant to the current query, temporal decay to prioritize recent information, and importance scoring to surface high-value memories regardless of recency.

Why does Agent Memory matter?

Without memory, every interaction starts from zero — the agent cannot learn from past mistakes, remember user preferences, or maintain context across sessions. Memory transforms a stateless text generator into a persistent assistant that improves over time and provides personalized, contextually appropriate responses.

How is Agent Memory used in practice?

Amazon Bedrock AgentCore provides managed memory that automatically extracts and stores key facts from conversations. A project management agent uses this to remember team members' roles, project deadlines, and past decisions — so when asked "What did we decide about the API design?" it retrieves the relevant discussion from three weeks ago without the user needing to repeat context.