Workflow data records how work moves from a request through decisions, actions and an outcome. For an AI agent, a linked exception case can be more useful than an isolated document because it shows what information was available and how success was checked. Licensing depends on the buyer’s task, usable history and permissions. Start with a description of one workflow; keep the underlying records in your systems.
- A useful case connects the original request, the information available at the time, the decision, the action and the observed result. It also identifies the relevant policy or system version. A folder of final answers loses the sequence that an agent needs to learn or be tested against.
- Origin helps owners organize a company-level description and consider a possible introduction. There is no standard price or guaranteed buyer for a workflow category. Request an initial review with company details; no files are needed.
What makes workflow data useful?
A useful case connects the original request, the information available at the time, the decision, the action and the observed result. It also identifies the relevant policy or system version. A folder of final answers loses the sequence that an agent needs to learn or be tested against.
For example, an accounts-payable exception might connect an invoice mismatch, the reviewer’s questions, an approved correction and a final posting. An industrial quote might connect a requested part, rejected substitutes, a revised offer and the eventual order. These are illustrative structures, not datasets available from Origin.
| Part of a case | What to describe |
|---|---|
| Starting state | What the team knew before acting; omit later answers from this view. |
| Decision and action | What changed, who or which role approved it, and the recorded reason. |
| Outcome | A verifiable final state, unresolved status, reversal or later callback. |
| Context | Policy version, dates, tool state and limits on what the user could do. |
Why are companies discussing this now?
micro1 has announced a $1 billion commitment to enterprise data acquisition over 12 months and describes using operational data for reinforcement-learning environments. An announced commitment signals direction; it is not evidence that the full amount has been spent or that every archive qualifies.
Mercor’s accounting benchmark uses tasks such as bookkeeping and month-end close within simulated companies. That supports interest in realistic work and testable outcomes. It does not establish a market price for an accounting firm’s raw files.
Source: micro1: enterprise data acquisition announcement, October 9, 2026
Start with a workflow your team understands
Choose one recurring process with exceptions and a recognizable finish. Look for cases that went wrong as well as routine successes. A difficult case with a documented correction may reveal more than thousands of repeated status updates.
- Accounting: reconciliation differences, invoice exceptions, reviewer corrections and final entries.
- Logistics: disputed charges, carrier responses, corrected classifications and verified settlements.
- Service: symptoms, readings, repair attempts, retests and callbacks.
- Distribution: RFQs, rejected substitutions, revisions, orders and returns.
- Manufacturing: defect evidence, corrective actions, process changes and retest outcomes.
Training, retrieval and evaluation need different scopes
Training changes a model using examples. Retrieval lets a system consult an accessible reference source while answering. Evaluation measures performance against cases and expected results. A simulation or reinforcement-learning environment additionally needs an executable task, available actions, feedback and a way to reset the starting state. A workflow archive does not automatically provide that environment.
Ask the recipient which use it wants, whether derived tasks or synthetic examples are allowed, and whether any held-out cases must stay separate. Read the AI evaluation licensing guide before agreeing that a testing-only request permits model training.
Map one case before preparing an export
Use the company record map to describe a workflow without uploading it. The blank workflow case manifest helps your team inventory starting conditions, linked actions, outcomes and known gaps locally. It is a preparation worksheet, not a data delivery specification.
Check missing links, changed identifiers, retention gaps and whether later edits overwrite the original record. Record unknowns plainly. Do not manufacture reasons that were never written down or silently treat a closed ticket as a successful outcome.
Explore fit with Origin
Origin helps owners organize a company-level description and consider a possible introduction. There is no standard price or guaranteed buyer for a workflow category. Request an initial review with company details; no files are needed.
Before access, your company and the proposed recipient need to resolve permitted uses, client restrictions, personal information, evaluation holdouts, derived material, retention and payment conditions. A first inquiry does not grant those permissions. AI teams can describe their data requirements.
Common questions
Are ordinary business records already agent training datasets?
Usually they still need assessment. A buyer must establish which task the records support, whether actions and outcomes remain linked, and whether the intended use is permitted. An archive is not automatically a runnable training environment.
Can synthetic data replace company workflow data?
Synthetic examples can reduce the need for routine records. Real cases may still help define tasks, test realism and expose difficult exceptions. The useful combination depends on the buyer’s task; volume alone does not establish value.
Sources and further reading
Origin Data Partners is independent and not affiliated with any buyer named on this page.