InfoQ has highlighted a production pattern for AI workflows that separates two needs that often conflict: durable execution in production and fast iteration during evaluation. Production systems need persisted, distributed steps that survive crashes, deploys, and restarts. Evaluation work needs lightweight runs so teams can quickly check whether an LLM output is improving.
The proposed approach is runtime-agnostic. Instead of tying business logic tightly to one durable workflow engine, teams define workflow steps in a way that can run under a production runtime or a faster local and evaluation runtime. That allows engineers to test prompts, tool choices, and model behavior without paying the full operational cost of a production execution path every time.
The pattern matters because AI application quality often improves through many small experiments. If every experiment has to move through the same heavy machinery as production, teams slow down and test less.
The trade-off is discipline. Runtime independence only helps when side effects, state, retries, and observability are designed deliberately, so evaluation shortcuts do not hide production failures.