The real shift in production workflows is at the evaluation layer, not the execution layer.

Getting an agent to run is easy. Knowing whether what it produced is actually correct before it touches a live system is the hard part. Most workflows optimise for making the automation run. The real leverage is in making it fail gracefully.

A supervisor agent pattern where an orchestrator delegates to specialised sub-agents and a validation layer checks every output against a JSON schema before anything gets written downstream catches the errors that actually matter. The other thing worth addressing is agent drift. When you chain multiple LLM calls, the model's interpretation of the original task degrades with each hop.

Re-injecting the original objective at every handoff fixed that in every production build I have run. Build the evaluation layer first and the rest of the workflow becomes significantly more reliable without any other changes.

How are people in this group handling output validation in production? What does your evaluation layer look like?

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The real shift in production workflows is at the evaluation layer, not the execution layer.