An AI Tool can look convincing long before it is dependable. A polished result proves that one input produced one acceptable output. Automation requires a stronger claim: the task boundary is stable, failures are visible, and a bad result can be recovered without corrupting later work.
The evaluation should therefore begin before model comparison. First define what the Tool is allowed to do.
Freeze the task contract
Write down four things:
- the input facts the Tool may use;
- the output fields it must return;
- the claims or transformations it must not invent;
- the condition a reviewer uses to accept the result.
For a product-description writer, the Tool may reorganize supplied facts but must not invent materials, certifications, performance claims, or customer outcomes. For a SQL debugger, the Tool may explain likely correctness and performance issues, but schema-dependent claims must remain conditional when the schema is missing.
Without this boundary, evaluation becomes a preference test. Reviewers reward fluent output even when the Tool solved a different task.
Test representative inputs before edge cases
Start with a small set of ordinary examples that reflect the intended use. The purpose is to determine whether the contract is coherent, not to hunt for exotic failures immediately.
Each case should preserve the same output requirements while changing the input facts. Score checks that can be observed directly: required fields present, supplied facts retained, unsupported claims absent, requested format followed.
Only after the representative cases pass should you add boundary cases: missing fields, contradictory facts, unusually long input, ambiguous instructions, or content that should be refused.
Separate deterministic checks from judgment
Some criteria can be evaluated exactly. A required heading exists or it does not. A JSON response validates or it does not. A product claim appears in the source facts or it does not.
Other criteria need a reviewer: whether a summary preserves the central caveat, whether a visual brief matches the intended mood, or whether an explanation is useful to the target reader.
Keep these groups separate. Deterministic failures should block acceptance automatically. Subjective scores should preserve the reviewer, rubric, and evidence so another person can understand the decision.
Test failure behavior, not only successful output
Before an AI Tool enters a Workflow, force at least one known failure. Confirm that the system does not pass partial output forward as if it were complete.
A safe failure should answer three questions:
- What failed?
- Which accepted inputs or outputs remain valid?
- What is the smallest action needed to retry?
If the only recovery path is “run everything again,” the Tool is not ready to become a Workflow node.
Promote evidence, not confidence
A Tool should move from demo to evaluated only when the published claim matches the evidence actually collected. A handful of controlled cases may support a narrow task claim. They do not support universal accuracy, provider uptime, or a ranking across unrelated tasks.
This restraint is useful product design. It tells users what they can rely on, tells developers what must remain observable, and prevents automation from amplifying an output that was never qualified for the next handoff.