Evaluate the tool action, not just the answer.

A convincing answer can hide a failed task. An assistant might say it updated a record after calling the wrong tool, selecting the wrong account or receiving an error. Evaluation needs to inspect what happened in the product as well as what the assistant said.

Why this question is timely

The 2026 Stack Overflow Developer Survey reports that trust in AI output is often conditional on being able to verify it. That is a useful product-design signal, rather than a measurement of searches for a particular keyword. For an assistant inside a SaaS product, verification should be part of the workflow.

Start with a tool inventory

UltraFit360’s AI Coach documentation separates tools for user context, history and actions, and describes intent routing for different request types. A SaaS team can begin with the same inventory exercise: which operations read records, which create a draft and which change something another person depends on?

Turn operations into evaluation cases

For each task, record the account context, user request, allowed tools, expected arguments and expected final state. Add a case where a required field is missing and the correct behaviour is to request clarification. Add a case where the user asks to operate on a record outside their account and the server must reject it.

Make retries observable

If a write tool succeeds and the response is lost, a repeated call must not silently duplicate the change. Give operations stable identifiers when appropriate and verify the resulting state. The assistant should distinguish a confirmed result from an uncertain one instead of inventing a success message.

Test retrieval and instruction boundaries

A retrieved document may include text that looks like an instruction. Include that condition in the evaluation set and check that it does not expand tool permissions or change the account scope. Application code remains responsible for authorisation even when the model chooses the intended tool.

Report outcomes alongside latency and cost

• Task success and correct final application state.
• Unauthorised operation attempts and server enforcement.
• Failure recovery and duplicate-write behaviour.
• End-to-end latency and total cost per completed task.

Run the same representative cases when changing prompts, models or tool definitions. The aim is an assistant whose actions can be inspected and corrected, with a clear explanation of what the evaluation does and does not cover.

Research: Stack Overflow 2026 AI trust data

Reference: OWASP GenAI security project