AI agent verification alternatives: build vs. buy.
Where Pruvz fits, what each approach does well, and when a narrow internal check is all you need: if you are deciding whether to build outcome verification internally or lean on tools you already run, this page is the map.
By Dor Sharoni, founder of Pruvz · Last reviewed on August 2, 2026.
Two ground rules. Every claim about another product is qualified and linked to that vendor's own published documentation, reviewed on August 2, 2026. And every material claim about Pruvz links to a published artifact you can inspect: the recorded product demo, the security and data architecture page, a real evidence packet captured from that demo, explained field by field, and the public packet schema and validator.
The category, in plain terms.
Five different jobs get mixed together in this conversation. None of them replaces the others, and most serious agent stacks will end up running several.
Observability and tracing
Shows how the agent ran: model calls, tool calls, latencies, errors. Built for the engineers who debug and improve the agent.
AI governance
Manages what agents are allowed to do: policies, risk assessments, access, approval requirements. Built for risk, legal, and compliance teams.
Preventive controls
Decides before the action happens: approval gates, guardrails, and permission checks that allow or block an agent at runtime.
Reconciliation and process intelligence
Compares records across systems after the fact, process by process and case by case, often with drill-down to individual events: did the process run the way it should, and where does it leak.
Business evidence
Proves what each individual action actually did: independently confirms the outcome in the system of record and keeps the decision-time context and the evidence trail.
The independence question: who grades the work?
Every verification approach answers one structural question differently: is the system that grades the outcome independent of the system that produced it?
Source-system-native verification is a real and useful pattern: Zendesk's Verified Resolution, covered in the comparison below, is one current example of a platform grading its own agent's work through an LLM-based verification process and pricing on the result. For measuring containment inside that platform, that is a legitimate design, and nothing on this page claims otherwise.
The trade-off appears when the number leaves the platform. A vendor grading its own agent's work is a self-reported metric: the same organization sells the agent, runs the workflow, and scores the result. That is not an accusation of bad faith; it is the same structural question your finance team asks about any self-reported number before it reaches a board deck. When the outcome feeds billing, audit, compliance, or an executive decision, someone eventually asks for a grader with no stake in the grade, checking the system of record where the outcome actually lives. That is the job an independent evidence layer exists to do, and it is why Pruvz never lets an agent's claim decide a verification result: only independent read-back does.
The alternatives, honestly: five ways teams verify AI agent outcomes.
Four established ways teams approach this today, plus the independent evidence layer. Each one earns its place in some stack; the boundaries are what this page is for.
Source-system-native verification
Example: Zendesk Verified ResolutionThe platform that runs the agent also verifies the result. The clearest current example is Zendesk's resolution model: conversations its AI agent handles are classified into tiers, and a resolution counts as verified when an LLM-based verification process evaluates the conversation after a defined inactivity window (up to 72 hours) and confirms the request was satisfactorily resolved. Verified resolutions are also the unit its outcome-based pricing bills on (per Zendesk's published documentation, reviewed August 2, 2026).
Where it shines: zero integration work, a meaningful containment metric, and pricing aligned with outcomes inside that platform's own workflow.
The boundary: scope and independence. The verification covers that vendor's own workflow, the grader is the platform that sells and runs the agent, and the check is an AI evaluation of the conversation rather than an independent read-back of an external system of record. The output is a reporting and billing metric, not a portable evidence record you can hand to an auditor.
Source: Zendesk resolution tiers documentation ↗Process intelligence and reconciliation platforms
Example: CelonisPlatforms like Celonis mine event data out of your source systems and reconstruct how processes actually ran: a living model of the end-to-end flow, with conformance checking against how the process should run, and, in their current direction, process context feeding AI agents and orchestration (per Celonis's published platform documentation, reviewed August 2, 2026).
Where it shines: aggregate truth. If the question is where the order-to-cash process leaks, which paths violate policy, or where the bottlenecks are across thousands of cases, this category is built exactly for that, across systems.
The boundary: Celonis is positioned around process intelligence, process adherence, and operating improvement across cases and systems. Its published platform model is not centered on producing a portable evidence record for each agent action, one that binds the original claim, the decision-time policy, independent read-backs, and a human review lifecycle; it can expose detailed case and event data, but that is a different primary product contract. Complementary to an evidence layer, not the same job.
Source: Celonis platform documentation ↗Internal build: orchestration plus read-back
Example: TemporalYour engineers own the workflow, so they add verification to it: run the agent inside a durable execution platform like Temporal, which preserves workflow state and resumes execution from the last durable point after crashes, network failures, or infrastructure outages, with configurable retry behavior (per Temporal's published documentation, reviewed August 2, 2026), and write a read-back step that checks the system of record after the action.
Where it shines: control and fit. A single read-back check against one system is genuinely simple to write, the team knows its own systems best, and execution reliability platforms are excellent at exactly what they promise.
The boundary: unless the verifier is deliberately separated, the workflow ends up grading its own homework. Internal builds often share the codebase, the owning team, the credentials, or the evidence store with the workflow they check; a genuinely independent verifier is possible, but it means designing and operating that separation explicitly. And the second workflow, the second system of record, and the first eventual-consistency incident start pulling the simple check toward the platform problems below.
Source: Temporal documentation ↗Warehouse data checks
Example: dbt testsThe data team writes tests over the warehouse: dbt data tests validate datasets after they are materialized, on the pipeline's schedule, and can store the failing rows for inspection (per dbt's published documentation, reviewed August 2, 2026). Reconciliation-style SQL comparing agent activity tables against billing tables fits naturally here.
Where it shines: dataset-level assurance in a stack your analysts already own. For questions like "do yesterday's refunds in the agent log match yesterday's refunds in billing, in aggregate", this is the cheapest credible answer.
The boundary: it evaluates warehouse copies after extraction and load, on the pipeline's schedule. A dbt test can identify and store the exact rows that failed, but it does not by itself bind a failing row to the agent's original claim, the decision-time policy, an independent source-system read-back, and a recorded human review decision. Those contracts and that lifecycle still have to be built around it.
Source: dbt data tests documentation ↗Independent evidence layer
PruvzA separate layer that records each high-impact agent action with the exact policy in force at decision time, independently reads the outcome back from the systems of record, read-only, and keeps every step as ordered, append-only evidence. This is what Pruvz does, and each part of that sentence is inspectable: the flow runs end to end in the recorded product demo, including a caught mismatch routed to human review; the trust boundaries are documented on the security page; and a packet captured from the product demo is published with the schema and validator to check it against.
Where it shines: independence and the record. The agent's claim never decides the result, mismatches get a review lifecycle whose decisions are appended rather than overwriting anything, and the output is per-action evidence that people outside engineering can read.
The boundary, stated as plainly as everyone else's: production connectors to live systems such as Stripe and HubSpot are the scope of the founding design-partner program, not something to buy off a shelf today; cryptographic sealing of exported packets is on the roadmap and is not claimed as a current fact; and Pruvz does not approve, block, or correct agent actions. It is the record, not the enforcement.
See it: a real evidence packet, field by fieldThe five approaches at a glance.
The same comparison, condensed: what each approach is for, who grades the outcome, what record it leaves, and when it is enough on its own.
| Approach | Primary job | Who grades the outcome | The record you get | Enough on its own when |
|---|---|---|---|---|
| Source-system-native verification (e.g., Zendesk Verified Resolution) | Resolve and grade conversations inside one platform. | The platform that runs the agent, via an LLM-based verification process. | A resolution tier on the conversation, used for reporting and billing. | The outcome lives entirely in that platform and its self-report satisfies whoever reads it. |
| Process intelligence and reconciliation (e.g., Celonis) | Reconstruct and improve end-to-end processes. | Analytics over event logs extracted from your systems. | Process models, conformance findings, and aggregate KPIs. | The primary need is process conformance, orchestration, and improvement across cases, rather than a portable evidence record for each agent action. |
| Internal build (e.g., Temporal plus your own read-back) | Reliable execution of your own workflow code. | Your own code and team, unless a separate verifier is deliberately built and operated. | Workflow histories and logs, read by engineers. | One workflow, one system of record, and engineering both owns and consumes the checks. |
| Warehouse checks (e.g., dbt tests) | Dataset quality and batch reconciliation in the warehouse. | SQL tests over synced copies of the source data. | Test results per run, with failing rows if stored. | Aggregate, after-the-fact assurance is acceptable and nobody needs per-action proof. |
| Independent evidence layer (Pruvz) | Per-action business outcome verification and evidence. | An independent layer reading the systems of record directly, read-only. | Ordered, append-only evidence packets with decision-time policy context and review state. | Outcomes cross systems, the readers are not the builders, or the record must be independent of the workflow. |
See what independent outcome verification produces.
The fastest way to judge the category is to look at its output. Watch a real agent run end to end, including the one where the agent reported success and the billing system showed no refund, then open the evidence packet captured from that same product demo and validate it against the public schema on your own machine.
These summaries describe each approach's primary purpose, based on the vendors' own published documentation, last reviewed on August 2, 2026. Individual platforms overlap and evolve. If you think this page describes a product inaccurately, write to hello@pruvz.ai and we will check and correct it. Written and maintained by Dor Sharoni, founder of Pruvz. Product names and trademarks belong to their respective owners; Pruvz is not affiliated with or endorsed by the companies referenced on this page.
"Why not build this internally?"
Sometimes you should, and this page will not pretend otherwise. A single API read-back is simple: if your one agent executes refunds in one billing system, a scheduled job that queries the refund and compares the amount is an afternoon of work, and it will genuinely catch the failure it was written for. If that is your whole problem, build it.
An evidence layer is what that job grows into once the answers have to survive more workflows, more systems, and readers who were not in the room. These are the parts that turn a check into a platform, and each links to the published artifact showing what the solved version looks like.
Capture contracts
Claims, receipts, and observations need a stable, versioned contract, or every agent change breaks your evidence. The public packet schema and validator show what that contract looks like once it exists.
Public schema and validator ↗Correlation
One business key has to tie the agent's claim, the execution receipt, and every read-back to the same action, across systems that never heard of each other.
See correlation in a real packetConnector normalization
Every system of record answers differently. Observations need one comparable model with server-assigned trust levels, so a claim can never masquerade as an independent observation.
The trust-level taxonomyRetries and eventual consistency
The refund may not be visible one second after execution. You need a verification window with retries, and the discipline to keep an unreadable source pending instead of calling it a mismatch.
The verification lifecyclePolicy snapshots
Six months later, the question is what policy was in force at decision time, not what the policy says today. That snapshot has to be captured at decision time and preserved without later records overwriting it.
A real decision-time snapshotEvidence ordering
An evidence trail that can be rewritten is an opinion. Records need append-only storage with atomic sequence numbers, so later facts are appended after the original ones, never over them.
The append-only timelineReview lifecycle
Mismatches need a queue, an owner, and a decision that is recorded as a new fact. A review ruling that silently overwrites the verification result destroys the record you built it for.
A caught mismatch, reviewedCoverage
The action with no evidence at all is the one that hurts. The layer has to know which actions it saw, which it verified, and where the gaps are, or silence reads as success.
How missing evidence is handledThree questions to ask any verification approach.
Including Pruvz. These are the questions that separate a verification story from a verification design, and any vendor or internal proposal should answer them in writing.
Which system is actually read back?
Ask this of any approach: does verification read the system of record where the outcome lives, or does it grade a conversation, a log, or a copy? Pruvz independently reads the systems of record the workflow touches, using read-only access. In the current product demo those are a billing system and a CRM system; production connectors for live systems of record, such as Stripe and HubSpot, will be selected and developed with founding design partners, and the verification engine is designed for systems of record such as CRM, billing, ERP, and ticketing.
Watch the read-back happen in the demoWhat is the verification window, and when does a result become final?
In Pruvz, a verification job is created when execution completes, off the agent's critical path. The verifier reads the systems of record inside a defined window with retries and backoff. A source that cannot be read yet keeps the action verification-pending; it is never misclassified as a business mismatch. The result becomes final on a terminal observation: verified, outcome mismatch, or verification failed. A final result states what the systems of record showed during that window; it is not silently re-checked afterwards.
The verification lifecycle, documentedWhat happens if the source system changes after verification?
The recorded result remains the record of what was observed and when, which is exactly what an audit needs it to be. Later facts, like a human review decision or an externally resolved case, are appended to the evidence trail as new entries; they never rewrite the original result. Automatic re-verification after a source-system change is a planned, explicit capability on the Pruvz roadmap: it is not current behavior, and no approach should claim continuous monitoring it does not do.
Appended, never rewritten: see the packet timelineA practical decision guide.
Two columns, stated plainly. If you are in the first one, build the check yourself.
Build a narrow internal check when
- One workflow executes against one system of record.
- Engineering both owns the check and consumes its results.
- A log line or dashboard is an acceptable record.
- Nobody outside the team audits the outcome.
- Losing the check's history would be an annoyance, not a finding.
Evaluate an evidence layer when
- Agent actions span several workflows or systems of record.
- Operations, compliance, or leadership read the results, not just engineers.
- Mismatches need a review lifecycle with recorded decisions.
- The record must be independent of the team that runs the agent.
- "Which policy was in force at decision time" is a question you get asked.
Judge it by the evidence, not this page.
The strongest argument for an independent evidence layer is inspecting one. Watch the recorded end-to-end demo, including the run where the agent reported success and the systems of record said otherwise, then download the packet that demo produced and validate it against the public schema on your own machine. The founding design-partner program is open: we are looking for 3-5 teams running agents with real business impact, and production connectors and initial partner workflows will be developed with them.