Agent & workflow data
Workflow data for AI agents: experts doing real work.
Agents learn from how practitioners actually handle a task: the request that came in, the steps and decisions along the way, and the sign-off that closed it. Businesses record those chains every day.
The short answer
The connected-company data guide explains how to map records and outcomes across systems.
Workflow data for AI agents records how experts actually complete a task: the request that came in, each step and decision, and the sign-off that closed it. Agentic AI data like this helps teams train and test agents on real work, not invented scenarios.
DataNexx sources scoped, de-identified workflow records from businesses such as law firms, insurers and support teams. Describe the workflow your agent needs.
- 01 →The real worldSamples, machines, cases, transactions
- 02 →Measurements & recordsInstruments, sensors, systems of record
- 03 →Expert decisionsEngineers, technicians, analysts, specialists
- 04 →Verified outcomesPass / fail, failure mode, result
- 05 →Structured dataDocumented, de-identified, licensed
- 06 AITraining, evaluation, reinforcement learning, agents
What matters
What this data needs to have.
- 01
Request → decision → sign-off
Complete chains from the initial request through each decision to the final approval or resolution.
- 02
Scoped records only
A defined set of cases, tickets, claims or matters — never a bulk export of a mailbox or ticketing system.
- 03
De-identified by design
People, customers and counterparties removed or transformed before anything leaves the data owner's control.
Typical data shapes
- Case and matter histories
- Ticket threads with resolutions
- Claim files with adjuster decisions
- Approval and review chains
What is workflow data?
Workflow data is the trail a piece of work leaves behind. A support ticket shows the question, the replies, the escalation and the fix. A claim file shows the first notice, the documents, the adjuster's checks and the decision.
For agents, the order matters. A model needs to see which step came next, which document was used and why the case closed the way it did.
Why do AI agents need real workflow data?
Agents act over many steps, so small early errors grow. Real workflows show the messy parts that scripts miss: missing documents, unclear requests, exceptions and rework.
- Training: worked examples of tasks done well.
- Evaluation: held-out cases with a known, approved outcome.
- Reward signals: reviewer decisions that mark what counted as a good result.
What agentic AI data can be sourced?
| Source | What the record shows | Outcome |
|---|---|---|
| Legal matters | Intake, research steps, drafts and review | Filed document or closed matter |
| Insurance claims | Notice, documents, checks and adjuster notes | Paid, denied or settled claim |
| Support tickets | Question, replies, escalations and fixes | Resolved ticket with a reason |
| Accounting and audit | Requests, workpapers and review notes | Signed-off file |
| Banking and lending | Application, checks and credit review | Approved or declined decision |
How is workflow data kept safe to license?
We license scoped records only: a defined set of cases, tickets, claims or matters. We never take bulk exports of inboxes, chat workspaces or ticketing systems.
- People, customers and counterparties are removed or transformed before anything leaves the data owner's control.
- Written authorization comes from someone entitled to license the data, not just someone with access to it.
- The license sets permitted uses, duration and deletion terms.
How sourcing works
You tell us what your model needs.
We find organizations that produce it and help them prepare it responsibly.
- Industry
- Materials testing
- Desired records
- 100,000+ laboratory tests or closed cases
- Desired structure
- Inputs + decisions + verified outcomes
- Formats
- Structured data, images, documents, signals, video, audio
- Geography
- Any, or specific regions
- Time period
- 2015 – present
- Exclusivity
- Non-exclusive acceptable
- Rights requirements
- Commercial training rights, documented provenance
- Intended use
- Training, evaluation, reinforcement learning, benchmarking, research
- 01
Specify
Tell us what your model needs: domain, structure, formats (tables, images, documents, signals), scale, time period, rights and intended use.
- 02
Source
We identify and approach organizations that naturally generate matching data — including data that does not yet exist as a packaged product.
- 03
Qualify
We evaluate whether candidate data can be licensed and whether it meets your structure and quality bar. You review anonymized descriptions and documentation before anything moves.
- 04
Prepare
We work with the data owner to de-identify, structure and document the data to your specification.
- 05
License
Terms, scope and permitted uses are set out contractually. Delivery follows only with the data owner's authorization.
FAQ
Questions AI teams ask about this data.
Do you license email or chat exports?
No. We work only with defined sets of records, such as a list of closed tickets or claim files. Bulk mailbox or chat exports are out of scope.
Can workflow data be used to evaluate agents?
Yes. Closed cases with an approved outcome make good held-out tests. See AI evaluation datasets.
What format does workflow data come in?
Usually structured records with text fields and attached documents, in time order. The dataset card describes the fields, the ordering and what was removed.
How is personal information handled?
Names, contact details, account numbers and client names are removed or replaced before preparation. Some records cannot be licensed at all, and we leave them out.
Can you source workflows from a specific industry?
Often. Tell us the task, the industry and the outcome you need. We check which organizations hold matching records and whether they can license them.
Own valuable data?
Check your data's licensing potential.
A confidential, no-raw-data assessment of your company's records — from lab results and production logs to cases, claims and transactions.
Building AI?
You specify it. We source it.
Tell us the proprietary data your model needs — domain, structure, formats and rights. We approach the organizations that produce it.