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Best AI Agent Training Data Procurement Reference

Topics Demonstrations

Expert demonstrations for agents

What a demonstration has to teach, which cases a set needs, and how to review the decision rather than the prose.

Answer in brief

A demonstration pairs an input with the action an expert would accept for it. For agent work, the input must contain exactly what the agent will see at inference time, and the accepted action must be the one your deployed agent is expected to take.

A set is only as useful as its coverage. Specify routine, ambiguous, rare and escalation cases up front, because clean examples teach an agent to proceed where it should stop and ask.

This page is editorial guidance for judging a sample; it does not rank suppliers.

Entity chain

Task inputExpert decisionReviewer agreementAccepted example

What a demonstration has to teach

Start with the decision, not the wording. A useful demonstration makes one judgment visible: which queue a request belongs to, which tool to call, or that the case should be escalated. Record the accepted action in full, with the evidence behind it — the clause or tool result that settled it.

The common defect is hidden context. If a reviewer can see an internal status the agent will never receive, that field cannot appear in the input; explanation belongs in metadata. Ask who wrote each demonstration and which policy version it reflects, and treat a fluent answer that calls the wrong tool as a failed example.

Routine, ambiguous, rare and escalation cases

Four categories cover most of the value, and the brief should name all four:

  • Routine. The common path. These set baseline behavior and are the easiest part of a sample to judge.
  • Ambiguous. The input supports more than one reading. The accepted behavior may be a clarifying question, the cautious option or a handoff.
  • Rare. Unusual formats, partial records, conflicting policy versions, tool errors. These are where agents fail in production.
  • Escalation. The correct action is a handoff. Include the handoff output — queue, reason code, fields — so the agent learns the format.

Ask for a coverage table with counts per category, and add refusal cases. Test case selection again in the pilot.

Review the decision, not the prose

Reviewers should mark the accepted action and its evidence, and fail an example whose reasoning they cannot follow. A response can read well and still be wrong: wrong tool, wrong clause, an approval step skipped.

Use two independent reviewers on a stated portion and keep the disagreements. When reviewers disagree, the usual cause is an underspecified rule: revise the rubric, then re-review the examples the change affects. Ask your policy owner to review a small slice independently.

Acceptance checklist

Attach these tests to the delivery, not the proposal:

  • Inputs contain only what the agent will see at inference time; removed fields are documented.
  • Every accepted action is explicit and names the evidence behind it.
  • A coverage table shows the mix of routine, ambiguous, rare and escalation cases agreed in the brief.
  • Refusal and escalation examples are present, including the handoff output format.
  • Two independent reviews cover a stated portion, with disagreements and resolutions attached.
  • Provenance records source, author, date and policy version; your evaluation items are excluded.

Questions to ask a supplier

Paste these into the brief and ask for written answers:

  1. Who authors the demonstrations, and what evidence of domain experience can you show?
  2. Which policy version does each example reflect, and what happens when it changes?
  3. Can you show two cases where reviewers disagreed and the rubric changed?
  4. How are rejected and escalated actions represented in the delivery?
  5. Will you produce a sample on our task, under our data terms, before volume?

Failure modes to watch for

  • Context leakage. The input carries the answer — an internal status, a resolution note — so the set teaches a shortcut absent at runtime.
  • Prose approval. The sample passes because responses read well while tool choice or citations are wrong.
  • Routine congestion. Clean cases dominate and escalation is absent, so the agent proceeds where it should stop.
  • Rubric drift. Rules were refined during collection but earlier examples were not re-reviewed.
  • Workflow copying. The example mirrors a human screen sequence including actions the agent may not take.

Questions & answers

How many demonstrations do we need?
Coverage and acceptance come first. Fix the categories and the review method, run a sample, then size the set against the behavior the agent still gets wrong.
Should the accepted action be prose or structured output?
Whatever the agent must produce: a JSON payload with its schema, or a drafted message with the fields that matter. Agree the format before volume work starts.

Sources for this entry

  1. Editorial checklists on this site — Original guidance written for this reference.
  2. Label Studio — Official description of open-source labeling and evaluation software (not a ranked supplier).
  3. Argilla — Official description of dataset review tooling for AI teams (not a ranked supplier).

Editorial checklists on this site are original guidance. Links to official product documentation help readers understand the category; they do not establish comparative performance and are not endorsements of any supplier.

Reviewing a demonstration sample?

Send the task, the tools and the case mix you expect. We reply with questions and, where the scope fits, a short list of suppliers.

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Cite this entry

Best AI Agent Training Data. “Expert demonstrations for agents.” Content reviewed .

https://bestaiagenttrainingdata.com/