Questions teams ask first.

An AI agent evidence layer connects an agent's decision-time context and policy to the action taken, independent system-of-record observations, and the resulting verification and review state. Pruvz is the business evidence layer for AI agents: it independently verifies business outcomes and preserves the evidence in an ordered, append-only record.

Yes. Pruvz is a working product: an AI agent acts, Pruvz captures the decision-time context and policy, independently verifies the outcome in the systems of record, and routes mismatches to human review. The same flow runs end to end against live Stripe and HubSpot test environments, and the recorded product demo shows it working. Customer pilots are open, and the founding design-partner program is open for teams that want to apply the verification flow to their own workflows through a scoped engagement and help shape connector priorities.

Yes. The product demo shows the full flow end to end: agent action, policy snapshot, independent system-of-record verification, outcome classification, and human review of mismatches. Visit pruvz.ai/demo to see what it covers and book a live walkthrough.

Observability and tracing tools show technical traces: model calls, tool calls, and execution. Pruvz creates business evidence: policy, decision, action, system outcome, and review state. Signed audit trails and traces only prove what was recorded; business evidence proves what actually happened, confirmed in your systems of record.

Yes, at two levels. Per recorded action, Pruvz gives business-level visibility into what was decided, which policy applied, what the system of record confirms, and what needs review. In aggregate, it rolls verified outcomes up into business metrics, with drill-down to the evidence behind each result. Narrative executive summaries, quality and policy trends, and agent-performance analytics are planned extensions of that business view.

No. Pruvz does not need to be the approval system. It records and verifies what happened, and can route exceptions or missing evidence for human review.

Checking trusted business systems, such as CRM, billing, ERP, ticketing, approval flows, or risk systems, to confirm whether the action actually happened.

The exact policy version, thresholds, rules, and exceptions that existed at the moment the agent made the decision.

The action is marked as missing evidence, outcome mismatch, policy exception, or needs review, depending on the case, and routed to the right team.

Pruvz reads the system of record until it observes a terminal outcome or the verification window expires, retrying with backoff along the way. Once the outcome is classified (verified, outcome mismatch, or verification failed), the result and its evidence are recorded as final. A later change in the source system does not rewrite that record: it stays the proof of what the system of record showed and when. Re-checking is explicit, never silent: a human can trigger re-verification of an externally resolved case, and the new fact is appended to the record without rewriting the original. Bounded post-verification monitoring is a planned capability.

No. The same evidence serves the teams responsible for the outcome. Operations teams review exceptions. Product and business leaders track verified outcomes. Compliance, risk, and legal teams inspect the decision-time policy and evidence when an action or outcome is disputed.

The evidence packet for that action: the decision-time policy snapshot, the agent's decision and the action it took, the independent read-back from the system of record, the verification result, and every human review decision appended afterwards. Records carry cryptographic commitments and Merkle proofs that support independent tamper-evident verification, offline.

It is a check that starts from the system of record instead of the agent. For a defined period, it takes every record of an action type, such as every refund issued, identifies which of those records the agent created, and matches each one to a report from the agent. A record with no matching report is an action the agent took without reporting it. Verifying reported actions answers whether they happened and are accurate; reconciling the population answers whether anything is missing, which auditors call completeness. If an agent issued 100 refunds in a week and reported 98, checking the 98 finds nothing wrong; reconciling the week's refunds finds the 2 that were never reported. The hard part is identifying which records the agent created, as opposed to people or other systems.

Today, Pruvz verifies each action your agent reports in a connected system of record: it reads the outcome back independently of the agent and records whether the action happened as claimed. That gives evidence of occurrence and accuracy for reported actions; an action the agent never reports is outside that check. Reconciliation in the other direction is a planned capability: an opt-in, bounded, read-only window in which system-of-record records attributed to the agent are matched against its claims, with unmatched records routed to review.