How to turn an agent-training requirement into a company search

Turn an agent-training requirement into a company search by defining the work the agent must perform, the information available at each step, the environment it acts in, the outcome used for evaluation, and the constraints on data access. “We need enterprise agent data” is too broad to identify a useful source.

The goal is to describe evidence a company may possess, not to assume a particular company has it. A sourcing brief should help distinguish a relevant source from a large but disconnected archive.

What must the agent do in the target workflow?

Describe the agent's job in a sentence that includes the situation and intended action. Examples might be triaging an equipment alert, updating a case after reviewing customer history, or proposing a code change in response to a defect. These are examples only. Specify what errors matter and what a human currently decides.

Avoid task labels such as “support,” “operations,” or “coding” on their own. Two teams with the same label can have different processes, tools and outcomes. Ask for an observable task boundary: what starts the work, what steps are in scope, and when the agent's turn ends.

Which records were available before the agent’s action?

List the source systems and fields available before each action. Distinguish information known at decision time from later corrections, outcomes or annotations. This prevents the source search from treating a final record as though the agent could have seen it earlier.

For a maintenance example, a brief may ask whether records include an initial alert, inspection notes, replacement parts, timestamps and a verified resolution. It should also ask which events are absent or captured only in attachments. The NIST AI Risk Management Framework's Measure function recommends documenting test sets, methods and metrics, but it does not prescribe a universal sourcing brief.

Which tools and operating conditions must the source represent?

Identify the tools, screens, APIs or physical settings in which the agent acts. State whether the task involves multiple systems, permissions, asynchronous events or human approval. If an environment can be simulated, say what must match the real workflow and what can be mocked.

Published environments such as WebArena and OSWorld illustrate that agent tasks can be evaluated within defined web or desktop environments. They demonstrate research designs, not access to real company data and not proof that a source matches your task. If the requirement is for historical operational events, say so separately from a live or simulated interactive environment.

How will the buyer judge success?

Specify what counts as success, what partial success means, and how a reviewer will verify it. Possible evidence includes a correct status change, a resolved issue, an accepted patch, or an action sequence satisfying stated constraints. If outcomes are subjective, define the rubric and reviewer process. A task without an observable outcome can still support exploratory work, but it is harder to compare consistently.

Keep the target answer or later outcome separate from inputs available to the agent. If those leak into the prompt, benchmark performance may be misleading. Ask how duplicates, revisions and related cases are grouped across training and evaluation splits.

Which company records and access conditions should sourcing check?

For each candidate company, identify likely systems, record families, date range, languages, geography, role context, retention and known gaps. Ask for a source description or schema before requesting a sample. The data owner must determine what it can disclose and to whom; an NDA or technical fit does not establish rights.

State constraints: customer or employee information, regulated content, confidentiality, redaction, access environment, retention, deletion and onward use. A first review may use a schema or aggregate description. Define what must be approved before raw content is considered.

What should the buyer sourcing brief contain?

How can a support-transcript request become a useful company search?

Instead of asking for “customer support transcripts,” an evaluation team might seek company-held case histories where the initial customer issue, agent response, escalation, final resolution and timestamps are linked. The target task could be recommending an escalation action using only the information available then. The brief would request a schema and describe how customer information is excluded or controlled before any sample.

This makes it possible to ask companies about source shape without claiming they have a suitable archive. A response is a lead for review, not proof of coverage, rights or model usefulness.

How should candidate sources be compared?

Do not write a requirement around an assumed supplier or a desired record count. Do not treat a company's public descriptions as verification of its actual source. Make unknowns explicit and use the same criteria across candidates. A good brief narrows the next question; it does not decide acceptance in advance.

For an owner who wants help positioning a potential licensing opportunity, Avelence's service includes research and dataset positioning. Any company introduction or buyer discussion depends on the owner's chosen scope and separate authority. A sourcing brief does not authorize disclosure or guarantee that a suitable company or buyer will be found.

Use the buyer sourcing brief to put that narrower requirement into one document. Its task, evidence, coverage and access fields help an approached company answer the same concrete questions as other candidate sources.