Context and Memory in AI: Why Your Agents Fail (and How to Fix Them)

AI agents fail because of poor context and memory management, not because of the model. Learn the difference between context engineering and memory engineering, and why it’s essential for building systems that work in production.

We explore the “lost in the middle” effect, memory-writing policies, retrieval that is conscious of the token budget, and why larger context windows don’t solve the underlying problem. A critical look at where agent architecture is headed.

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🤖 AI-generated content: the script, voices, and images for this episode were produced using artificial intelligence tools.

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