A common failure mode in AI-assisted work is designing verification after the output already exists.
We give an AI agent a task, inspect the finished result, and ask:
“Does this look good?”
But a polished result is not evidence of a correct result.
Verification should begin before execution. Start with the end goal—not with the prompt or the deliverable.
Define what you will verify
Before delegating work to an AI agent, answer five questions.
What outcome are we trying to achieve?
Describe an observable business or user outcome, not only an artifact to produce.
What evidence would support accepting the result?
Decide which source data, tests, comparisons, or observations will support the decision. Evidence should be traceable to something outside the agent’s confidence in its own output.
What would cause us to reject it?
Define unacceptable results and missing evidence before seeing the answer. Otherwise, a convincing output can quietly lower the standard.
How deeply should we verify it?
Verification depth should increase with the consequence of failure, difficulty of reversal, and blast radius.
Where is human judgment still required?
Name a qualified owner who understands the context and consequences. The agent that produced the result should not be its sole verifier. Agreement from another AI is not independent evidence by itself.
Turn the questions into a verification contract
Imagine an AI agent analyzing customer feedback and recommending the next product priority.
The intended outcome is not simply a clear report. It is an evidence-backed view of customer problems for a defined segment and period.
The required evidence might include traceable source feedback, coverage across the relevant customer segments, disclosed exclusions, counterevidence, and an explanation of how duplicate feedback was handled.
The result should be rejected when claims cannot be traced to their sources, important segments are missing, exclusions are unexplained, or conclusions go beyond the evidence.
The process can verify whether the evidence is accurate and whether the agreed method was followed. It cannot prove that one product priority is strategically correct. A qualified product owner still has to evaluate strategy, cost, opportunity, and trade-offs before accepting the recommendation.
The same distinction applies to engineering. Code can pass every specified test and still implement the wrong business rule.
Match verification to the risk
For low-impact, reversible work with a narrow blast radius, spot checks and a quick human review may be sufficient.
As consequence, irreversibility, or blast radius increases, stronger controls may be needed: representative test cases, authoritative source comparisons, independent review, permission limits, staged execution, approval gates, rollback where possible, and monitoring.
These controls are not interchangeable guarantees. Monitoring may detect a failure only after harm has occurred, and some actions cannot be rolled back. For an irreversible, high-impact action, the right control may be mandatory human approval—or preventing autonomous execution entirely.
Automated checks have boundaries
Automated checks can test encoded, observable conditions. They may still be incomplete or wrong, and they cannot establish that those conditions represent the correct business objective.
This is why verification needs business context as well as technical checks. Someone must understand what success means, what failure would affect, and which uncertainty remains after the checks pass.
Decide whether AI should do the work
If we cannot define the intended outcome, identify independent evidence, specify rejection criteria, or find a qualified owner to review the consequences, the AI should remain advisory—or not be used for that task.
AI-assisted work should not begin with “What should I ask the agent to do?”
It should begin with “How will we decide whether the result is acceptable?”
Verification is not the ceremony at the end of AI-assisted work. It is the control system that makes the work possible.