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From Chains to Trees: Why LLM Agents Need Structural Memory

Logs are useful. They are also lazy. A business agent that fails halfway through a product search, customer-support flow, compliance checklist, or research workflow will usually leave behind a long trace: thought, action, observation, thought, action, observation. The standard instinct is to read the failed trace as a chain. This step followed that step; the final reward was bad; therefore the chain was bad. Very tidy. Also very wasteful. ...

April 9, 2026 · 18 min · Zelina
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From Retry to Recovery: Teaching AI Agents to Learn from Their Own Mistakes

A failed automation run usually tells you more than a successful one. A coding agent compiles the wrong program and receives a concrete error. A web-navigation agent clicks into the wrong product page and sees that the attributes do not match. A task agent tries an invalid action and the environment complains, patiently, like a machine that has seen too much. In each case, the system does not merely say “failed.” It gives clues. ...

March 18, 2026 · 17 min · Zelina