Seller field guide · 3 min read
Can business workflows become AI agent evaluation tasks?
A workflow may support evaluation when it can be turned into a clear task with permitted starting context, observable outcomes and defensible success criteria. Historical records alone are not a finished benchmark. Confirm the intended use, rights and limitations before proposing a dataset for evaluation.
Define what success would mean
NIST’s AI Technology Evaluation program describes comparing model performance across meaningful tasks and datasets using consistent metrics. For a company archive, the first practical question is whether the work has an outcome that an evaluator could observe rather than guess.
A task such as route this request to the correct review queue has a different success condition from write a helpful response. Identify the applicable policy, permissible actions and the evidence that would show completion. Where judgment is unavoidable, state who would judge it and against what criteria.
Source notes: NIST: AI Technology Evaluation program
Separate the starting state from the historical outcome
A historical record often contains information added after the decision. Keep that future information out of the proposed starting context. Otherwise the exercise may reveal the answer rather than test whether an agent can perform the task from the information available at the time.
The following is a design checklist for discussing a candidate with a buyer. It is not a claim that QuoteMyData has validated a benchmark or that every business process can be evaluated automatically.
| Task component | What to define | Failure to avoid |
|---|---|---|
| Initial state | Permitted facts available at decision time | Including the later answer in the prompt |
| Instructions | Task and applicable policy version | Using current rules to judge an older process silently |
| Actions | Operations the evaluated agent may perform | Assuming unrestricted production access |
| Success criteria | Observable result or documented review rubric | Equating a historical action with the only valid answer |
| Limitations | Ambiguous cases and missing information | Presenting incomplete records as a validated benchmark |
Source notes: NIST: AI Risk Management Framework
Specify evaluation rights and data separation
The NIST program explicitly separates evaluation data from training use. Ask a prospective buyer whether your material would be held out, used for training as well, or combined with other records. Put the actual proposed rights and handling into the scope discussion instead of relying on the generic phrase AI use.
Keep confidential and identifying information outside the task wherever possible, and review any remaining risk. A realistic workflow does not require live customer access. Start with a bounded description and approved sample, then let the technical evaluator determine whether a useful test can be designed.
Source notes: NIST: AI Technology Evaluation program · NIST: Privacy Framework
Illustrative scenario · fictional company
A fictional procurement workflow becomes a candidate task
Cypress Manufacturing has records of routing supplier requests to the correct reviewer. The team proposes a candidate evaluation using the request category and the procedure version in force at that time. The historical routing outcome is kept separate from the initial context, and ambiguous cases are flagged rather than automatically labelled correct. Bank forms and supplier identifiers are excluded. The result is material for a buyer to evaluate, not a claim that a valid benchmark has already been established.
When to pause or narrow the scope
Client work and customer files are excluded from this starting scope. Assess company-owned internal records separately from personnel, health, privileged or NDA-restricted material.
- The proposed task has no interpretable outcome or review criteria.
- Missing historical context makes it impossible to define a fair starting state.
- The rights or handling requested exceed what the company can authorize.
Your preparation checklist
- Define the task and observable success criteria.
- Separate initial context from later outcomes.
- Record the applicable policy and allowed actions.
- Flag ambiguous examples and missing information.
- Confirm evaluation versus training rights and the handling of held-out data.
Start with a description of the records. The quote form does not ask you to upload a dataset.
Discuss your dataSources and further reading
- NIST: AI Technology Evaluation program · Checked October 26, 2026
- NIST: AI Risk Management Framework · Checked October 26, 2026
- NIST: Privacy Framework · Checked October 26, 2026
This is general information, not legal advice or confirmation that a buyer will accept your records. Published buyer ranges describe their programs, not an offer for your dataset. Buyers may pay QuoteMyData when a deal closes; see how we are paid.
