Start with the workflow, not the model
The best production test begins by describing the real job: who will use the output, what data is allowed, what decisions are forbidden, and what failure would be unacceptable.
- Define success and critical failure before running prompts.
- Include real edge cases, not only clean examples.
- Separate customer-facing, internal, and system-to-system outputs.
Run a small but serious eval
Use 20 to 50 representative cases, at least two candidate agents, repeated runs for unstable tasks, and human review notes. Track both score and failure tags.
Decide the launch mode
If evidence is strong, launch with monitoring. If quality is mixed, use draft-only mode. If critical failures are frequent, redesign the workflow before adding more automation.
How to reuse the method
evaluation methodology can become a small internal evaluation. The point is not to create the largest task set. The point is to cover real work, real risk, and real output formats.
- Define unacceptable failures first.
- Prepare representative samples next.
- Compare candidates with one shared rubric.
What evidence to keep
Save the input, prompt version, model version, run date, raw output, human rating, and failure tags. This makes future retesting and stakeholder review much easier.
Pre-launch checklist
Before using this method in production, run a small retest with real inputs, edge cases, and a plan for what happens when the agent fails.
- Is there a clear human-review rule?
- Are model version and evaluation date recorded?
- Which outputs are not allowed to be sent or written automatically?
- Is there a fallback path when the agent fails?
A practical next step
If you are evaluating this method, start with ten real samples: three normal cases, three edge cases, two high-risk cases, and two cases with strict language or formatting requirements. Run two or three candidate agents and compare quality, repair time, and critical failures.