PrismWorks · October 5, 2026 · 5-minute read
Start with the decision the business must make.
Consider an agent assigned to handle an approved invoice. Its summary says the task is complete. The business still needs to establish which beneficiary was used, whether the amount was correct, whether the action was authorized, and what the provider actually recorded.
Those are acceptance questions. They connect the business requirement to the resulting state. A useful verification system makes that connection explicit and gives a reviewer the evidence needed to assess it.
Correctness belongs to the workflow.
A journal can balance while using the wrong account. A payment can match an invoice while naming the wrong beneficiary. A credit can be individually correct while duplicating an earlier adjustment. Each example illustrates the same principle: arithmetic or response quality alone is too narrow a definition of success.
The specification must identify the required outcome, the permitted effects, and the observations that establish both. Finance defines the obligation and policy. Engineering makes the requirements executable. Reviewers decide whether the evidence supports acceptance.
The standard for accepting autonomous work should be the quality of its evidence.
Independence is an architectural property.
The agent being evaluated should not control the expected result or the protected checker. Source observations should have a clear origin and appropriate access boundaries. An agent’s explanation remains useful for diagnosis, while trusted system observations establish what the checks can conclude.
This separation also makes disagreement useful. If the agent reports success but a required source record is absent, the discrepancy becomes something to investigate. It should not disappear inside an aggregate score.
Uncertainty needs a first-class result.
Suppose a payment provider receives an instruction, but the response is lost. A timeout establishes that the caller did not receive a response. It does not establish whether the provider acted. An automatic retry could create another financial effect.
The workflow needs a way to preserve that uncertainty and identify the observation required to resolve it. Pass, fail, and inconclusive serve different purposes. An inconclusive result can be the most accurate and operationally useful answer available.
Evidence should survive a change in the system.
Models, prompts, tools, policies, and source mappings change. A release decision should identify the versions and conditions that were evaluated, the failures exercised, and the observations retained.
Historical evidence replay can recheck recorded observations. It cannot recreate every external effect or establish that a future run will behave identically. The value is traceability: teams can explain what was tested, compare a candidate change, and identify where fresh evidence is needed.
Expand authority only when the evidence supports it.
Begin with one workflow and a declared scope. Test normal cases, exceptions, and missing evidence. Measure false approvals, false blocks, review effort, and operating cost. Keep native permissions and authorized review in place while the scope is evaluated.
A successful evaluation supports a decision about that scope. Production execution also needs accepted native controls, recovery behavior, and operational ownership. Making those responsibilities explicit is part of a credible path to automation.
Our product follows this principle.
Databridle connects work specifications, controlled tests, independent checks, and retained evidence. Our first commercial focus is a supervised accounts payable pilot. It provides a concrete setting for finance and engineering to define correctness together and examine the resulting evidence.
Explore Databridle, examine the verification architecture, or use the acceptance checklist to structure your own review.