for enterprise · ai governance
your agents' numbers become evidence, not testimony.
Enterprises are moving AI agents into workflows where a wrong number has a name attached: financial close, supply commitments, pricing, compliance reporting. Then the auditor asks the question nobody can answer: how do you know the agent's number was right? Today the answer is testimony; the model seemed confident. quplane makes the answer evidence.
the ai audit trail
what the record contains
Every computation an agent delegates to quplane returns with an audit trail a reviewer can verify without re-deriving the work.
fingerprint
A content hash of exactly the data that was read, so the record binds to the input and nothing else.
instruments
Which verified solvers ran, each gated against a reference implementation before it was allowed to serve.
certificate
The typed contract: exact, tolerance bounded, or heuristic, with the agreement between independent instruments on the record.
verdict or hole
ACT when the evidence clears the gate; otherwise the exact hole, named: which reading disagreed, where, and what would repair it.
agentic ai governance
what your governance framework asks, and what the record answers
Model risk management asks how automated outputs are validated: the validation is the gate each solver passed before serving, on the record. Audit controls ask for documentation of automated decisions: the certificate is stored with the answer, machine readable, reproducible from the fingerprint. Challenge processes ask whether outputs can be independently confirmed: independent instruments must agree before any verdict says act, and disagreement is reported as a named hole rather than smoothed over. Documentation duties for AI systems ask what the system does and how it fails: this one states both, per answer, every time.
The refusal is the feature. Under adversarial questioning in our verification battery, the engine refused every question the data could not support: no invented onsets, no fabricated rates, no clusters that were not there. An agent that cannot say "the evidence does not support this" is a liability generator; one that says it with reasons is a control. This is what verifiable AI agents mean in practice: not a model that promises, but a harness that proves.
And the whole trail stays in your boundary: the engine runs as an AMI in your own AWS account, no data reaches the vendor, and procurement runs through AWS Marketplace on the bill you already pay.
See real solves with the certificates attached, read what a certifiable agent harness is, or point your platform team at the calling convention.