One signal

Jeremy Daly argues that AI agent workflows need not send every decision to an expensive reasoning model. In his article, he describes using lightweight models for parallel checks such as test coverage and validation, while leaving policy routing to ordinary code.

Why it matters

Daly says he tested TypeSafe’s Jev alongside an open-source alternative. His practical point is to reserve heavier reasoning for the tasks that need it, rather than asking one model to do everything. It is a practitioner’s argument, not a general performance verdict. But it gives developers a useful design question: where does a model add judgement, and where is straightforward code the better tool?

Discuss: Which routine decisions in an AI agent workflow would you trust to a lightweight model, and which should stay in explicit code?

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