Giving AI agents memory that improves future runs

AI Engineer16m 38s
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    Video summary

    Jake Broekhuizen distinguishes stored traces from memory an agent can use on a future run. Semantic memory covers facts and preferences, episodic memory covers examples and experiences, and procedural memory covers instructions and skills. Working context is temporary, while long-term context guides later behavior.

    Jake Broekhuizen describes a read-write cycle: load relevant context, capture an agent's trajectory, filter the evidence for useful signal, then update durable context. A financial-services assistant that slips from measured guidance into directive language illustrates why observation alone does not correct future behavior.

    Jake Broekhuizen maps the loop onto LangSmith observability, background analysis and a context hub. Most trace data should remain history or evaluation data rather than become memory. He warns that stale runtime caches can hide updates and recommends human review before changing high-impact instructions or policies.

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    Jake Broekhuizen in a plain blue T-shirt on black beside the blue and white headline “Agents That Learn”. Framed in blue with WWW.ARTIFICIAL-INTELLIGENCE.VIDEO, 8 October 2026 and duration 16m 38s.

    Jake Broekhuizen explains a capture, analyze and update loop that turns selected agent experiences into durable context while protecting important behavioral rules.