AI agents act. Pruvz proves what happened.
AI agent outcome verification against systems of record.
Turn every high-impact agent action into a verified business record: what the agent saw, which policy applied, what it did, and what your systems of record confirm.
Then turn verified actions into a trusted view of agent performance and business outcomes.
Refund approved
Agent approved a $42 refund. Billing transaction, CRM ticket, policy snapshot, and approval threshold were verified.
| Action | Policy | Evidence | Status |
|---|---|---|---|
| Refund approved | Refund v3.2 | CRM + Billing | Pruvz Verified |
| Claim rejected | Claims v5 | Docs + CRM | Needs Review |
| Plan upgraded | Sales v2 | CRM + Billing | Pruvz Verified |
The accountability gap is becoming the enterprise blocker for AI agents.
Agents can decide, call tools, update records, approve workflows, and trigger business actions. But when a manager, auditor, regulator, or customer asks "why did this happen?" raw traces are not enough.
Pruvz creates the business proof layer between agent activity and enterprise accountability.
A self-reported claim from the agent. Useful for debugging, not enough for business accountability.
Independent records from trusted systems, linked into a single evidence packet.
Shows activity, but not necessarily the business justification or final outcome.
Shows what happened, why it was allowed, and whether the system of record confirms it.
The policy may have changed since the action happened.
Preserve the policy, thresholds, and exceptions that existed at the moment of decision.
Not another agent trace tool. A business evidence layer.
Signed audit trails and technical traces only prove what was recorded: model calls, tool calls, and workflow execution. Pruvz creates business evidence that proves what actually happened: policy snapshot, system-of-record outcome, review status, and a complete evidence packet business teams can understand.
The full story behind every important agent action.
A structured business record that connects context, policy, decision, action, outcome, and review, not just a transcript of agent activity.
What the agent saw
User request, retrieved data, customer context, documents, previous cases, and relevant business state.
Which policy applied
The exact policy version, thresholds, rules, exceptions, and approval requirements used at decision time.
What decision was made
The agent's business decision, decision reason, allowed action, blocked action, or required escalation.
Which action was executed
Tool calls and business actions such as refunds, claim updates, plan changes, approvals, or CRM updates.
What actually happened
Verified outcomes from systems of record such as CRM, billing, ERP, ticketing, approval flows, and risk systems.
What needs review
Exceptions, missing evidence, threshold violations, suspicious actions, and items requiring human review.
Observability, governance, and the evidence layer
Three distinct jobs, with different primary owners. Observability shows how the agent ran. Governance manages policy and risk. Pruvz verifies that the business action actually happened and what it means, then rolls it up for the teams accountable for the result.
This comparison reflects each category's primary purpose and typical native capabilities. Individual platforms may overlap across categories.
Pruvz is not another trace or policy tool.
It is the layer that turns high-impact agent actions into verified business records, and it works alongside the observability and governance tools you already run. For the full picture, including build-vs-buy, read the honest comparison: category, alternatives, and build vs. buy.
We don't trust the agent's claim. Pruvz is built to verify the business outcome.
An agent can say it refunded a customer, rejected a claim, changed a plan, or updated a CRM record. Pruvz treats that as a claim, not proof. Proof comes from trusted systems: CRM, billing, ERP, ticketing, approval flows, risk systems, email gateways, and internal records.
How Pruvz works.
Pruvz sits beside your agents and enterprise systems, creating a durable evidence trail for every high-impact decision and action. Non-blocking integration through an SDK or API, with asynchronous evidence collection.
Capture context
Record the user request, retrieved data, model decision, tool intent, and relevant business context.
Snapshot policy
Preserve the exact policy version, rules, thresholds, and exceptions used at decision time.
Verify outcome
Check trusted systems such as CRM, billing, ERP, ticketing, risk engines, and approval flows.
Review evidence
Route exceptions and missing evidence to human review with a complete, readable evidence packet attached.
Built for product, business, operations, and compliance teams, with the evidence technical teams need.
Run production AI agents with confidence, every high-impact action gets a verified business record.
Give engineering observability, compliance accountability, and operations complete visibility into every high-impact agent action through a business control room, without forcing anyone to read raw agent traces.
Pruvz Evidence Packet
Every high-impact agent action gets a verified business record: context, policy, decision, action, and a confirmed system-of-record outcome, on an ordered, append-only evidence trail, ready for review.
Replay any high-impact agent action from context and policy to the verified business outcome.
Want the real thing? Inspect a real exported evidence packet, field by field, and validate it against the public schema on your own machine.
When evidence is incomplete or inconsistent
Most actions are verified automatically. When Pruvz detects missing evidence, policy exceptions, or outcome mismatches, the action is routed for human review, while verified actions remain trusted, with the full evidence trail behind them.
What works today, what we build with partners, what comes next.
No vague roadmap language: the verification flow below runs end to end today, and every product capability claim belongs to one of these three delivery stages. Illustrative product data is labeled separately.
Shown end to end in the product demo
- Full verification flow: agent action captured, executed, and independently verified
- Decision-time policy snapshots, immutable and bound to each action
- Independent system-of-record read-back of billing and CRM outcomes
- Ordered, append-only evidence packets for every action
- Outcome mismatch detection with human review decisions on record
- Business overview built from verified outcomes, with drill-down to evidence
What we build with founding design partners
- Production system-of-record connectors, starting with billing and CRM platforms such as Stripe and HubSpot
- Applying the verification flow to a partner's real agent workflow
- Shaping the evidence model, review flow, and integration priorities
On the roadmap
- Cryptographic sealing and external anchoring for tamper-evident records
- Additional system-of-record connectors: ERP, ticketing, approval flows, and risk systems
- Optional re-verification of already-final results
Want the full picture? Explore the product demo or talk to us about the design-partner program.
Turn verified agent actions into a trusted business view.
Every verified agent action becomes part of a trusted business view. Pruvz connects the relevant data across your agents, policies, workflows, and systems of record, then turns it into summaries, metrics, trends, and recurring patterns, so product and business teams can see whether agents are delivering the intended results.
Executive summaries
A clear summary of what your agents did, what outcomes they produced, what changed, and what needs attention.
Business outcome metrics
Track resolutions, confirmed outcomes, escalations, refunds, approvals, conversions, and other workflow KPIs, measured against verified evidence.
Quality and policy trends
See policy alignment, evidence completeness, outcome mismatches, repeated exceptions, and how agent behavior changes over time.
Drill down to evidence
Move from any metric, trend, or anomaly directly into the verified actions and evidence packets behind it.
Every metric traces back to the verified evidence behind it, not a model's self-report.
Agent resolution quality improved 8% this month. Most failed outcomes were eligibility checks in the refund workflow. Escalations rose for Tier 3 customers during the period following the latest policy update. Further review is recommended.
- Refund approval rate increased
- Repeated eligibility exceptions detected
- One workflow producing higher escalations
- Policy v3.2 showing a lower outcome-mismatch rate than v3.1
For workflows where "the agent said so" is not enough.
Start with one high-impact workflow, prove accountability, then expand across your agent operations.
Billing & refunds
Refund approvals, billing corrections, subscription changes, credits, and payment-related agent actions.
Claims operations
Claim intake, claim rejection, policy eligibility, approval thresholds, and exceptions that need human review.
Customer support
Escalations, account updates, compensation, plan changes, retention offers, and SLA-driven decisions.
Risk & governance
Policy exceptions, audit requests, compliance reviews, approval proofs, and internal investigations.
Agent visibility
Business-level visibility into individual agent actions and aggregate performance: what agents decided, which policies applied, the outcomes they produced, and where behavior or business results are changing over time.
Built for any agent your business needs to trust.
If your business relies on what an agent answers, decides, recommends, or does, Pruvz helps you understand it, verify it, and improve it.
Where Pruvz creates value
- ✓Customer-facing agents answering questions or resolving requests.
- ✓Agents making recommendations, classifications, or business decisions.
- ✓Agents updating systems or executing business workflows.
- ✓Knowledge assistants working with approved organizational content.
- ✓Any AI workflow where quality, visibility, or outcomes matter.
What Pruvz gives your teams
- ✓A verified record of important agent outputs and actions.
- ✓Visibility into the knowledge, policies, and context behind each result.
- ✓Summaries, metrics, and trends across agents and workflows.
- ✓Clear exceptions and items that need attention.
- ✓Drill-down from every important metric to the underlying evidence.
Designed for enterprise security reviews.
Pruvz separates agent execution from independent verification: your agent acts in your systems, and Pruvz verifies the outcome with read-only access, keeping ordered, append-only evidence. Security and data handling are part of the architecture, not add-ons. Read the full security and data architecture.
Execution and verification, separated
Your agent executes the business action; Pruvz independently verifies it by reading the system of record. Agents can submit claims, but only Pruvz's verification service assigns a final result.
Read-only verification access
Pruvz verifies by reading, not writing: it executes no business actions in your systems. In the production model, the agent's execution credentials and Pruvz's least-privilege verification credentials are never the same identity.
Append-only, ordered evidence
Claims, independent read-back observations, evaluations, and human review decisions are appended as ordered evidence with server-assigned trust levels; recorded evidence is never rewritten. Cryptographic sealing with integrity hashes is on the roadmap, so records are designed to be tamper-evident.
Data minimization by design
Pruvz captures what proves the action, context, policy, decision, and outcome. The production deployment model includes redaction and tokenization of sensitive fields before evidence is stored.
Encryption and least privilege
The production deployment model is designed to provide encryption in transit and at rest, tenant-scoped access and authorization boundaries, and least-privilege, reviewable access to evidence.
Built for enterprise security reviews
Designed to support enterprise security reviews and SOC 2 / ISO 27001-aligned environments, with configurable retention, deletion, and hosting options where commercially supported.
Questions teams ask first.
How Pruvz differs from agent observability and tracing, what it verifies, and what happens when evidence is missing.
Is Pruvz available today?
Pruvz runs as a working end-to-end product demo today: 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. Our founding design-partner program is open to 3-5 teams that will apply the verification flow to selected real workflows through a scoped POC, using agreed staging or test environments, and help shape production connectors.
Can I see Pruvz working?
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.
How is Pruvz different from agent observability and tracing?
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.
Does Pruvz give visibility into what my agents are doing?
Yes, at two levels. Per action, Pruvz gives business-level visibility into every high-impact agent action: what was decided, which policy applied, what the system of record confirms, and what needs review. In aggregate, it rolls those verified actions up into a business view with executive summaries, business outcome metrics, and quality and policy trends, and you can drill down from any metric or trend straight into the verified evidence behind it. It is visibility your business teams can read, not just a technical trace.
Does Pruvz approve or block actions?
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.
What is system-of-record verification?
Checking trusted business systems, such as CRM, billing, ERP, ticketing, approval flows, or risk systems, to confirm whether the action actually happened.
What is a policy snapshot?
The exact policy version, thresholds, rules, and exceptions that existed at the moment the agent made the decision.
What happens when evidence is missing?
The action is marked as missing evidence, outcome mismatch, policy exception, or needs review, depending on the case, and routed to the right team.
When is a verification result final?
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 an already-final result is an explicit, planned capability, not silent background monitoring.
Is Pruvz only for audit and compliance?
No. Beyond per-action evidence, Pruvz aggregates verified activity into a business view: executive summaries, business outcome metrics, and quality and policy trends, so product and business teams can see whether agents are producing the right results, then drill from any number into the evidence behind it.
Can Pruvz evaluate customer-facing agent answers?
Yes, when answer quality can be evaluated against approved knowledge, business policies, required disclosures, customer context, or measurable downstream outcomes. Pruvz connects the answer to the evidence used to produce it and surfaces recurring quality and business-impact patterns. It does not claim to judge whether any answer is universally correct.
Become a founding Pruvz design partner.
We're looking for 3-5 enterprise teams deploying AI agents in high-impact workflows. Help shape the evidence model, verification flows, and system-of-record integrations Pruvz will bring to production.
What you get
- The Pruvz verification flow applied to one of your high-impact agent workflows
- Evidence packets, mismatch detection, and human review for that workflow
- Direct influence on the evidence model, connectors, and roadmap
- Priority access as Pruvz moves toward general availability
What we need from you
- One high-impact agent workflow, such as refunds, claims, or support actions
- Access to the relevant systems of record in a staging or test environment
- A short feedback loop with the team that owns the workflow
How the engagement runs
- A structured proof of concept, scoped in weeks rather than quarters
- Success criteria agreed up front: which actions get verified, against which systems
- Weekly working sessions with the founder
- Next step: a 30-minute intro call to map your workflow to the evidence model
Make your AI agents provable.
Join our design partner conversations and help shape how enterprises monitor, verify, and review AI agent actions in production.