TIL: Break AI Workflows Into Parts You Can Grade

The all-in-one prompt feels efficient until it fails.

Analyze the ticket, infer sentiment, classify the issue, decide urgency, draft the customer response, suggest internal follow-up, update the CRM tags, and return perfect JSON.

It might work in a demo. Then one field is wrong in production and nobody knows what to fix.

The response might be empathetic but factually sloppy. The category might be right and the urgency wrong. The follow-up might be good but the customer-facing answer might promise something the company cannot do. A single pass/fail grade hides too much.

Break the work into smaller contracts:

1. classify the issue
2. extract facts and missing context
3. decide urgency
4. draft the response
5. validate the draft against policy
6. produce internal follow-up actions

Each step gets its own prompt, schema, examples, traces, and evals. You can improve the classifier without changing the response writer, add stricter policy validation without touching extraction, and replay one step instead of burning the whole run again.

Do not split things just to draw more boxes. Split when a step has its own success criteria, failure shape, owner, or risk level.

Agent workflows benefit from the same split. They get less magical, and much easier to debug, when they have named states and inspectable transitions. The model can still do useful work; it just does the work inside smaller boxes.

If you cannot grade the parts, you cannot improve the whole thing with any precision. Split complex AI work where the evaluation signal changes.

Related: AI evals and AI platforms.

Part of the Effective AI Engineering series.

Source: adapted from Mirascope’s “Break Complex Tasks into Evaluable Components”, MIT licensed.