Designing Reliable LLM Agents With Deterministic Control Flow
Every engineer who's shipped an LLM agent past the demo stage hits the same wall. Fifty lines of Python, an API key, a system prompt, and two tools is enough to build something that looks autonomous and feels magical. Point that same architecture at tens of thousands of real users, and the magic turns into 2 a.m. pages.
I call this the Production AI Wall. It's the moment a team realizes the property that makes LLMs impressive in a demo, their probabilistic, "creative" behavior, is the same property that makes them unreliable infrastructure. Traditional systems engineering is built on a simple contract: input *A* plus state *S* produces output *B*, every time. LLMs don't honor that contract. They sample a probability distribution over tokens.
When we let an LLM freely control its own loop, deciding which tool to call, how to parse the response, when the task is...
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