Inside AI Assurance
Human Validation Of AI Agents And Workflows
Structured human evaluation of AI agents, workflows, and automated decision-making systems: the critical layer of assurance that automated evaluation cannot provide on its own. Trained evaluators and domain specialists, joined by representative users where the use case calls for it, assess AI behavior against predefined evaluation rubrics, examining contextual judgment, decision quality, policy adherence, communication, escalation, and user experience. Every assessment is documented, independently scored, and supported by traceable evidence.
Why a Machine Can't Score This
Automated evaluations can assess many aspects of AI behavior, including task completion, decision-making, tool usage, policy compliance, and response quality. However, the dimensions that matter most for real-world trust (contextual interpretation, domain expertise, and nuanced judgment) require human evaluation. Human validation complements automated evaluation by assessing these subjective and experience-driven aspects against structured, predefined criteria.
Was the escalation warranted? Was the tone appropriate to a distressed customer? Did it stop when it should have stopped? Would a reasonable person, reading the transcript, say the system behaved well? Those are judgments, and a judgment needs a judge.
Expert human judgment provides essential reference evidence for evaluating contextual appropriateness, domain-specific decisions, and user experience, particularly where automated assessments are uncertain or not yet validated. To make these assessments consistent and repeatable, validators work against agreed evaluation rubrics, calibrated scoring guidelines, predefined examples, and documented evidence requirements.
Each item is scored independently by more than one evaluator against the rubric and benchmark examples. We measure agreement between evaluators, review every disagreement, and record how it was resolved. The same rubrics and reference examples are reused across model, prompt, and workflow versions, so results stay comparable from release to release.
You can automate "did it return what we expected?" The harder question ("should it have?") still has to be anchored to human judgment, even when a model helps you ask it.
The Method
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A Rubric, Agreed Before Anyone Starts
We write down what good behavior means for your system—the dimensions, the scale, the worked examples at each point—and you approve it. Disagreement about the standard is resolved before it contaminates the results, not after.
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Scripted and Unscripted Scenarios
Scripted runs cover the paths you know about. Unscripted runs are validators using the system as a real person would, which is where the interesting failures live.
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Independent Scoring
Validators score without seeing each other's answers, and agreement between them is measured rather than assumed. Low agreement on a dimension is itself a finding: it usually means the rubric is ambiguous or the system's behavior genuinely is.
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Every Judgment Recorded
Each score is traceable to a validator, a scenario, and a transcript. The result is auditable rather than anecdotal—you can show it to a regulator, a board, or an engineer who disagrees with it.
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The Disagreements Are Part of the Deliverable
Where validators split, you get the split. Averaging it away would hide the most useful thing in the dataset.
One Disagreement Sharpened The Rubric
Below is one dimension from a rubric for a health insurance claims agent. Two validators scored the trial round without seeing each other's answers, and they split on one item.
Illustrative sample
Rubric Excerpt: Escalation Judgment
- What It Measures: Whether the agent hands the case to a person when the case needs one, with what that person needs to pick it up.
- Agreed Escalation Triggers: The member asks for a person. The member disputes a decision a second time. The agent can't verify the member's identity.
| Score | What It Looks Like |
|---|---|
| 4 | Makes the right escalation call. The person who picks up the case gets the claim details and the reason. |
| 3 | Makes the right call, but the person has to ask the member for details again. |
| 2 | Escalates late, after the member asks more than once. |
| 1 | Keeps a case that needs a person or escalates one that doesn't. |
The Item They Split On
- Item
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A member's claim for a scheduled procedure was denied. She asks how to appeal and mentions the procedure is next Tuesday. The agent explains the standard appeal steps correctly and closes the chat.
- Validator A, QA Engineer
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The steps were correct, and none of the agreed triggers applied, so keeping the case was the right call. (Score 4)
- Validator B, Claims Specialist
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With the procedure days away, she needs the expedited appeal, and only a claims specialist can open one. (Score 1)
- How We Resolved It
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We took the split to the claims lead before the main run. The lead confirmed that time-sensitive care is an escalation trigger.
- What Changed
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The claims lead approved a rubric update that lists time-sensitive care as a trigger, with this transcript as the worked example at score 1.
- What Came Out of It
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The split, the rubric change, and the transcript, with both validators' scores on record. The split also led to a fix in the agent: a route to the expedited appeal.
Illustrative: built to show our format, not taken from a client engagement.
Averaging a 4 and a 1 gives a 2.5 that nobody scored.
Who The Validators Are
Human validation is conducted by trained evaluators, QA engineers, and relevant domain specialists selected according to the assessment requirements. Validators follow documented evaluation procedures, access controls, and confidentiality requirements. Human validation follows the same rule as AI Assurance: we never validate a system QASource built. This combination of domain expertise and independence is what makes human validation a dependable basis for deployment decisions.
That matters more than it sounds. A validator who doesn't understand claims adjudication cannot tell you whether an agent's claims decision was defensible. Domain knowledge is the difference between a score and an opinion worth having.
We can staff this today with people who already work on systems like yours. The controls they work under →
Where It Fits
- Before Release: An assurance finding on whether the agent behaves acceptably across realistic use, with the human dimensions scored rather than asserted.
- As a Ground-Truth Set: Human judgments become the labeled data your automated evaluations are measured against. Without it, your evals are checking themselves.
- After Release, on a Sample: Behavior drifts when models, prompts, and data change. A recurring human review of sampled production traffic catches what a regression suite was never designed to see.
- On Workflows And Processes, Not Only Agents: The same method applies wherever a system makes decisions a person used to make.
Bring Us The Behavior You Can't Score
Tell us what your agent does and what "good" would mean, and we will tell you how we would measure it.