AI uses for records
Why recorded outcomes make business records useful for grading AI agents
By SourceX Editorial · Updated
Short answer
Recorded outcomes make business records useful for grading AI agents because they supply the reference answer: outcome data such as a ticket resolution code, an invoice paid date or an NCR disposition shows what really happened. If a system wrote down how the work ended, the record can grade an agent's attempt; if not, it can only illustrate the task.
Key takeaways
- An outcome is a recorded result a grader can check without asking the people involved.
- Good outcome fields are written at the time, dated, linked to the task and final or versioned.
- Recorded failures, such as reopened tickets, callbacks and denied claims, are as useful for grading as recorded successes.
- Auto-close rules and reused status values are the most common reasons outcome fields mislead.
- Label or exclude unresolved records rather than guessing how they ended.
What counts as a recorded outcome?#
A recorded outcome is a field or event that states how a piece of work ended, captured at the time and linked to the task it closes. A grader can compare an agent's result with it without interviewing anyone.
Not every status qualifies. A ticket marked closed tells you work stopped; a resolution code that says refund issued, bug confirmed or configuration corrected tells you what happened. The test is whether a reviewer who never worked at the company could use the field to decide whether an attempt was right.
- Written by a system or a named reviewer, not inferred months later.
- Dated, so it can be placed after the task and the steps.
- Linked by an identifier to the request it resolves.
- Final, or with its change history kept when it was revised.
- Able to tell success from failure, not just open from closed.
Outcome fields by record family#
Outcome fields differ by record family, but most operational systems hold at least one. The table lists common fields, the systems that usually store them and what a grader checks against each.
| Record family | Outcome field | Typical systems | What a grader checks |
|---|---|---|---|
| Support tickets | Resolution code, reopen flag, solved date | Zendesk, Intercom, Freshdesk, Jira Service Management | Whether the agent's fix matches the resolution and avoids a reopen |
| Bug and issue records | Merged commit, passing tests, linked release | Jira, Linear, GitHub, GitLab | Whether the proposed change passes the tests the real fix passed |
| Invoices | Paid date, short-pay reason, dispute code | NetSuite, QuickBooks, Acumatica | Whether the billing action led to payment rather than dispute |
| Service jobs | Completion status, callback under warranty | ServiceTitan, Housecall Pro, FieldEdge | Whether the diagnosis held or the customer called back |
| NCRs | Disposition: use as is, rework, scrap or return to vendor | QMS and ERP quality modules | Whether the recommended disposition matches the approved one |
| Warranty claims | Approved, denied or no fault found, with failure code | ERP warranty modules, returns and lab records | Whether a recommended claim decision matches the approved one and the lab finding |
| Sales opportunities | Closed won or closed lost with reason | Salesforce, HubSpot | Whether the qualification call matches how the deal ended |
Why graders need outcomes, not just activity#
Graders need outcomes because activity alone cannot separate a good attempt from a bad one. An activity log shows that a technician added notes and a dispatcher reassigned a job; only the outcome shows whether the repair held.
Many agent developers prefer rewards that can be checked without human judgment: the code passes its tests, the invoice gets paid, the part passes reinspection. Business records with recorded outcomes supply those checks for everyday tasks where no public answer key exists.
Outcomes also keep grading honest. When the reference answer comes from what really happened, an agent cannot score well by producing text that merely sounds right. That is why a smaller set of records with trustworthy outcomes often beats a larger set without them.
Resolved versus unresolved records#
Resolved and unresolved records should be separated before any review, because they play different roles in grading. Resolved records act as reference answers; unresolved ones are, at best, practice tasks with no answer attached.
The middle rows are where most archives need work. An auto-closed ticket looks resolved in a report but carries no evidence that anything was fixed.
| Record state | How it can be used | How to handle it |
|---|---|---|
| Resolved, success recorded | Reference answer for grading | Keep with its full history and outcome field |
| Resolved, failure recorded | Shows what a wrong path looks like | Keep and label clearly, such as reopened or callback |
| Closed by an automatic rule | Unreliable as an outcome | Exclude or flag unless another field confirms the result |
| Still open or abandoned | Task without an answer | Exclude from grading sets or label as unresolved |
| Outcome only in free text | Usable after review | Extract the result into a field and record the method |
Where outcome fields go wrong#
Outcome fields go wrong in predictable ways, and most problems come from how a system was configured rather than how people worked. Check for these before describing your records as outcome-rich to anyone outside the company.
- Auto-close rules that mark tickets solved after a period of customer silence.
- Status values reused over time, so the same code meant different things before and after a process change.
- Outcomes stored in another system, such as payment in the ERP while the job sits in field service software.
- Overwritten fields with no history, where a disposition was changed and the original is lost.
- Default values staff never changed, such as a resolution code left on the first menu option.
- Migrations that mapped old outcome codes to new ones without keeping a record of the mapping.
Illustrative: an equipment maker audits its warranty outcomes#
Illustrative: a fictional maker of commercial kitchen equipment handles warranty claims from independent service agents in its ERP's warranty module, tests returned parts in a returns lab and pays agents through accounts payable. Its VP of engineering assumes that years of closed claims make a strong grading set for an agent that recommends claim decisions.
An audit of field metadata finds three problems. Claims were closed automatically when a service agent missed a paperwork deadline, so closed did not mean decided. The failure code list was rebuilt after a product redesign, with no record of how old codes map to new ones. And the most telling result, whether the lab confirmed the failure or found no fault, sits in a spreadsheet keyed by return authorization number.
The team excludes administratively closed claims, writes down the old-to-new code mapping and joins lab results to claims on the return authorization number, holding unmatched claims apart. End-customer names and site addresses are flagged for removal during preparation. The package is smaller than the raw archive, but each claim now carries a dated decision and, where a part came back, a lab finding. The VP also makes the failure code mandatory at claim close, so future periods need less cleanup.
How SourceX looks at outcomes#
SourceX looks at outcomes early, because they bear directly on drivers in the SourceX Enterprise Data Value Framework: AI utility, since graded records support more uses, and data cleanliness, since consistent outcome fields need less repair. Outcomes that must be reconstructed add preparation cost, which reduces net value. The first fit check asks which systems hold outcome fields and how consistently they are filled in, using metadata only.
Records that move forward are prepared and approved under the SourceX five-step transaction: Supply, Rights, Preparation, Approval and Delivery. For outcome-rich records, Preparation does the heavy lifting. It writes down how each outcome was derived, including code mappings, joins and exclusions, so a buyer can trace every reference answer back to its source. The SourceX Evidence Packet carries that derivation in its provenance record, next to licensing rights, permitted use, the privacy record and release authorization, and the company signs off on all of it before delivery.
Frequently asked questions
Is a customer satisfaction score a recorded outcome?
Partly. A satisfaction rating shows how the customer felt, which is useful context, but it does not confirm the problem was solved. It works best alongside a resolution code or a reopen flag rather than as the only outcome on a record.
Can outcomes be reconstructed after the fact?
Sometimes. A payment in the ERP can confirm an invoice outcome, and a later ticket from the same customer can reveal a reopen. Reconstruction is acceptable when it follows documented rules and is labeled as derived, so a developer knows it was not recorded at the time.
Do we need to share our grading rules as well?
It helps. Policies, service commitments and QA criteria explain why an outcome counted as success. Sensitive rules can be summarized or generalized during preparation, and the company approves what is shared.
What if outcomes live in a different system than the task?
That is common and fixable when the two systems share an identifier such as a job number, order number or ticket ID. Document the join, check a sample of matches by hand, and keep unmatched records separate rather than guessing.
Are older records with weak outcome fields still useful?
Yes, for some uses. Older records can still show how tasks were described and handled, which helps copilots and workflow examples. Label them as ungraded so they are not mixed into evaluation sets.
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