Colvin: There is massive opportunity in an agent optimization state layer
Samuel Colvin · ⚡️Monty: the ultrafast Python interpreter by Agents for Agents — Samuel Colvin, Pydantic · Mar 14, 2026 · at 28:34
Samuel Colvin discusses the commercial strategy of Pydantic Logfire, moving beyond basic evals toward continuous, persistent agent optimization.
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“I think that there is an enormous opportunity for effectively a new layer of state within applications. You could think of it as memory, but it's often a lot more than memory. It's the like current state of agent optimization, some of which will be code, some of which will be model choice and model settings, some of which will be system prompt.”
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