Agent Memory and State Management
Agents without memory forget everything between invocations. For agents that need to remember user preferences, learn from past interactions, or maintain context across sessions, you need a memory architecture.
Types of Agent Memory​
| Memory Type | Duration | Storage | Use Case |
|---|---|---|---|
| Working memory | Single session | In-context (prompt) | Current conversation, intermediate results |
| Short-term memory | Hours to days | DynamoDB with TTL | Recent interactions, session context |
| Long-term memory | Permanent | DynamoDB / S3 | User preferences, learned patterns, facts |
| Episodic memory | Permanent | Vector store | Past experiences retrievable by similarity |
| Semantic memory | Permanent | Knowledge graph / vector store | Domain knowledge, entity relationships |
Key Design Decisions​
- What to remember - Not everything is worth storing. Filter for actionable insights.
- When to retrieve - Every turn? Only when relevant? Similarity threshold?
- How much context - More memory in the prompt means higher cost and potential distraction.
- When to forget - TTL policies, relevance decay, explicit user deletion (GDPR).
What This Course Covers​
| Module | Topic |
|---|---|
| 1 | Memory architecture patterns |
| 2 | Working memory with conversation buffers |
| 3 | Short-term memory with DynamoDB and TTL |
| 4 | Long-term memory with vector stores |
| 5 | Episodic memory: learning from past interactions |
Premium
Agent Memory and State Management
Get the complete 5-module course with DynamoDB, vector store, and episodic memory implementations for production AI agents.