Start with small fixtures

You do not need a giant benchmark to improve an internal AI workflow. Start with ten to twenty examples that represent the real failure modes: missing fields, ambiguous requests, conflicting documents, stale context, and tool errors.

The goal is repeatability. If a prompt change breaks a known case, you should know before users do.

Evaluate the workflow, not just the final text

For agents, correctness includes tool selection, retrieved sources, structured output validity, fallback behavior, and whether a human approval step was triggered.

ts
Expected:
- asks for missing invoice ID
- does not call refund tool
- returns needs_context status

Try the idea

Guardrail chooser

Pick a workflow risk level and see the safer rollout pattern.

medium risk workflow
  • Require schema output
  • Add retry limit
  • Ask user before external write

Use evals as a rollout gate

Before expanding access, run fixtures against prompt changes, retrieval changes, model changes, and tool permission changes.

Production notes for AI workflows

AI workflows need product boundaries as much as model prompts. Decide what the system can answer, which tools it can call, what data it can use, and when a human must approve the result.

Logs, structured outputs, evaluation fixtures, fallback states, and cost limits make the workflow operable after the first impressive demo.

Conclusion

Small evals are not academic ceremony. They are a practical way to keep AI workflows from regressing quietly.

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