Build agents to learn from experiences using Amazon Bedrock AgentCore episodic memory

This article explains how Amazon Bedrock AgentCore episodic memory enables AI agents to learn from past experiences and improve performance over time.

• Episodic memory captures complete reasoning paths: goals, actions, outcomes, and reflections from agent interactions
• Two-stage extraction: turn-level processing captures individual exchanges; episode-level synthesis creates coherent narratives
• Reflection module identifies patterns across multiple episodes to generate generalizable strategic insights
• Custom configurations support domain-specific memory handling via prompts, model selection, and hierarchical namespaces
• Benchmarks show +11.4% improvement in Pass^1 and +13.6% in Pass^3 over baseline across retail and airline domains
• Episodes work best for specific step-by-step guidance; reflections excel for strategic decision-making
• Ideal for complex multi-step tasks and repetitive workflows; unnecessary for simple one-time questions

Episodic memory complements other AgentCore memory types to help agents build on successful strategies and avoid repeating mistakes.