How to evaluate an AI workflow before scaling it

Choose the smallest useful task

Describe the trigger, input, expected output, and person who uses it. Start with a draft or recommendation when that lets you learn without changing records or sending messages. If fixed steps can solve the task, test that approach before adding an agent that chooses its own steps. Anthropic’s architecture guide distinguishes these two patterns and recommends starting simply.

Source context: Anthropic’s architecture guide.

Record the baseline and build evaluation cases

Measure the current process, including review and rework. Create a set of representative examples with an expected result or scoring rubric. Include ambiguous requests, missing information, stale sources, and cases the system should refuse or escalate. Keep some cases separate from the examples used to improve the workflow.

Define what an accepted result means

Write concrete checks for correctness, source support, completeness, and permitted behavior. Identify failures that must block release even if average quality is high. Score the same cases for the baseline and proposed approach. Record disagreements between reviewers so the rubric improves rather than hiding them in an average.

Inspect data access and action permissions

Map where inputs travel, what the system may read, and what it can change. Give it only the access required for the task. Specify who approves consequential actions, how a reviewer sees the evidence, and how access can be revoked. NIST’s voluntary AI Risk Management Framework provides broader context for managing risk throughout the lifecycle; this short guide is not a compliance assessment.

Source context: NIST AI Risk Management Framework.

Run a bounded pilot and make a release decision

Track accepted results, review time, rework, latency, and operating cost. Investigate failures by type rather than only counting successes. Expand only when the acceptance criteria are met and an owner can monitor the deployed workflow. Document a stop condition, fallback process, and re-evaluation trigger when data, permissions, or models change.