Agent Memory Management Patterns
Agent memory management determines how AI agents maintain context, remember decisions, and handle state across long conversations or multi-step tasks. Patterns include short-term context immediate conversation, long-term storage facts and decisions, semantic compression summarize to reduce tokens, and selective retrieval recall relevant context. Effective memory patterns improve consistency, reduce redundant reasoning, and enable complex multi-step workflows. Key insight: hierarchical organization detail → summary → principle enables token-efficient long conversations.
- Radar Signals0