Evaluate an AI workerbefore live work.
Evaluate an AI Worker against one defined workflow before it takes on live responsibility. Test normal work, incomplete inputs, exceptions, approval boundaries, and the evidence a process owner needs to decide what changes next.
An evaluation plan is an operating aid—not a safety, accuracy, or performance guarantee.
Review the workflow under the conditions it will actually meet.
Begin with a shared definition of done and a named person who can inspect the output. Then test the normal path and the cases where the Worker needs more context, approval, or a human decision.
- 01Normal path
- Use representative inputs and confirm the intended output is complete, useful, and traceable.
- 02Incomplete input
- Check that missing context is surfaced instead of being silently filled in or sent onward.
- 03Exception path
- Introduce an unusual case and verify that it pauses with the right context for the named owner.
- 04Approval boundary
- Confirm that actions needing review remain queued until the appropriate person decides.
- 05Change decision
- Record what should stay, change, or stop before the Worker takes on a broader responsibility.
Make the expansion decision reviewable.
- 01
Define the job to evaluate
Name one recurring workflow, its trigger, intended output, and definition of done. A broad role is harder to evaluate than a bounded responsibility.
- 02
Choose evidence before the first run
Decide what the owner will inspect: source context, output, approval state, exception path, and any follow-up required.
- 03
Test the routine and the edges
Include ordinary cases, incomplete inputs, conflicting instructions, and situations that should pause for a person.
- 04
Review the boundary, not just the output
Useful output is not enough. Check whether the Worker stayed within its agreed tools, actions, review rules, and escalation path.
- 05
Decide what changes next
The workflow owner can keep the scope, revise it, or stop it. Expansion should be a separate, explicit decision.
Let the process owner decide what the evidence means.
The goal is not to prove that an AI Worker can do everything. It is to make one workflow, its limits, and its next decision visible to the people accountable for the work.
Normal cases meet the agreed definition of done.
Incomplete or unusual cases pause with useful context.
Approval-required actions remain reviewable before completion.
Any scope change has a named owner and explicit decision.
Scope the first pilot and set its approval levels before testing.
Bring the recurring work your team wants to evaluate before it grows.
We can map the workflow, its evidence, review points, and exception path before defining what a Worker should handle.
Common AI worker evaluation questions
Evaluate one defined workflow against representative normal cases, incomplete inputs, exceptions, approval rules, and an agreed definition of done. The process owner should be able to inspect the evidence before the scope expands.
The process owner should review it with anyone accountable for approvals or exceptions. They are the people who can judge the workflow output and decide whether a boundary should change.
No. A demo can show a possible path, but an evaluation should also test the cases that are incomplete, unusual, or outside the agreed boundary.
The workflow should pause or escalate to the named person with enough context to decide the next action. Record whether the boundary, inputs, or review rule needs to change.
No. It is a practical evaluation framework, not a guarantee. The appropriate scope, controls, and review requirements depend on the workflow and its potential consequences.