Schema and business invariants
Check required fields, types, relationships and domain constraints against the accepted data contract.
Data operations verification
Transform the data.
Preserve the contract.
Verify agent-driven data work against schema, reconciliation, privacy and lineage constraints using controlled fixtures and outcome checks.
An executable acceptance contract
The expected outcome.
The evidence to accept it.
Specify what a data task must produce and what it must preserve. Exercise transformations and updates against controlled fixtures, then check the resulting data and its provenance.
Keep your agent and business systems. Specify the permitted work, reproduce the conditions and evaluate the observed result with Databridle.
What to verify
The checks that
define correctness.
Reconciliation and completeness
Compare expected inputs, outputs and totals. Detect omitted records, duplicate writes and unintended changes.
Permitted data use
Define approved sources, destinations and access scope. Test whether the workflow respects those constraints under adversarial inputs.
Versioned provenance
Retain input/output references, transformation versions and independent observations. Reproduce the check with the original accepted contract.
Example failure case
Make the hidden
failure observable.
A reconciliation agent produces balanced totals while silently dropping a required account. Record-level completeness checks expose the omission despite a plausible summary.
The verdict stays bound to the work contract, source observations and coverage. Missing required evidence is inconclusive.
Connect verification to your workflow
A shared contract.
A clear decision owner.
Bring one transformation or reconciliation, approved fixtures, a data contract and the data owner. Agree the independent observations required to accept the output.
Compare agent releases under the same declared setup. Retain repeated runs, costs, coverage and replayable evidence for the acceptance review.
Use the verification checklist ↗Explore verification cases ↗
Maintained verification
Acceptance that stays
with the workflow.
Use platform access and a maintained workflow package to keep environments, business checks and supported adapters aligned with each release. Agree source coverage, execution and retention allowances, and integration ownership.
Engineering owns the release decision. The domain/control owner accepts correctness rules, and security approves test access. Compare changes using independent observations and retained evidence.
Explore maintained coverage ↗Make autonomous work verifiable
Make your acceptance criteria executable.
Connect your agent workflow, source systems and required outcomes to Databridle.