Giving AI Agents Memory That Learns — Jake Broekhuizen, LangChain
Most teams are great at watching what their agents did. Far fewer turn that into lessons for the next run.
Jake Broekhuizen from LangChain's Labs team explains how to give agents memory that actually changes their behavior. Using a real financial services agent whose tone kept slipping from advisory to pushy, he shows why traces and logs are only evidence: they become memory only when a lesson turns into durable context the agent reads next time. He breaks memory into semantic, episodic and procedural types, separates working memory from long-term memory, and walks through the read-write cycle: read context, run, filter the evidence for signal, and write it back. He shows how LangSmith and Context Hub capture, analyze and update that context, and shares three hard-won lessons about what to keep, caching, and when a human should review changes.
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