The question of how to trust AI agents with real business work has produced five distinct kinds of answer. They solve different problems, and most serious production stacks use more than one. This page describes each honestly — including what each does well and where it stops.
No vendor names anywhere on this page. Approaches are described, not competitors.
Runs an AI agent through structured scenarios before it goes live — adversarial prompts, edge cases, known-hard inputs — measuring how it behaves and where it fails. The output is a report on the AI's behaviour under controlled conditions.
A model that lies about tool availability, a workflow that loops on ambiguous inputs, a prompt-injection vulnerability — all findable before production.
It tells you about the AI agent's behaviour in the lab, not about what happens when the AI meets your real systems. A test suite that shows an agent behaves correctly against a mock CRM tells you nothing about what happens when a novel case appears in your real CRM at 2am. Testing is a floor, not a ceiling.
You're evaluating whether to deploy an AI system at all, or comparing two options against a benchmark.
Records what an AI agent does in production — traces, logs, prompt/response histories, latency, cost — so operators can debug, monitor, and audit after the fact.
Performance issues, cost anomalies, prompt drift, regressions after a model update — observability is how teams see them.
A wrong action is recorded, not prevented. And the records are held by the operator, so they can be edited, replayed, or backdated — which is fine for debugging and inadequate when the reader is a customer or an auditor who doesn't trust your storage.
You need to understand and improve how your AI systems behave over time.
Sits after the agent has acted and checks whether the claimed outcome actually happened in the systems of record. A refund the agent says it processed is checked against the payment provider; a ticket the agent says it resolved is checked against the support tool. The result is a decision — verified, not verified, or duplicate — that the workflow can use to decide what to tell the customer.
Catching false completions after the fact, before the wrong message reaches the customer or the ticket closes. Signed receipts of what was verified become audit evidence, and the check happens across systems the agent itself doesn't control — a genuinely independent read.
The check happens after the agent has already tried the action. If the attempt was wrong — a refund to the wrong customer, an amount below policy, a duplicate retry that already went through — the verification catches the completion claim, but the original action was still attempted. It also cannot see actions the agent should have taken and didn't (the silent step problem — nothing to verify against nothing).
You're running agents that already have permission to act, and your primary concern is that they don't lie or hallucinate about the outcome; the actions themselves are either reversible or already governed by some other layer.
Structures agent work into workflows, roles, and reviews — with a second AI agent checking the first one's output before it reaches a human or a downstream system.
Workflows become inspectable, scope becomes explicit, and reviews catch obvious failures the working AI didn't.
The reviewing AI agent shares training data, blind spots, and confidence patterns with the first agent it reviews. Two systems that fail together — fluently, and at the same time — do not check each other; they agree with each other. Adding a third doesn't fix this. Agreement is not verification, and calling it that is the trap.
The work is genuinely hard for one AI agent, and a second opinion (even from another AI) is better than none — provided the reviewer's opinion isn't treated as proof.
Places a deterministic program between the AI's proposal and the outside world. The AI agent drafts; the program enforces rules; a person approves the parts that matter; an independent read-only identity verifies the outcome against the system of record.
Not detected after, but blocked before — and producing evidence a customer, accountant, or auditor can check without trusting the vendor.
It governs actions taken through this system, not through other tools using the same credentials. Grounded is not the same as correct. And changes made directly in your books after the fact are detected, not prevented — no vendor should be able to block you editing your own records.
The AI's work meets real systems and real consequences — real customers, real money, real records — and "we said we did it" is not a strong enough claim.
Testing and observability solve problems for the operator; governed execution solves the problem of proving to someone else that the work was right. A team building seriously with AI probably wants all five eventually — but knowing which job each is doing is more important than owning all five immediately.
Post-hoc verification pairs particularly naturally with governed execution: govern the actions before, verify the outcomes after.
Related: What is governed AI execution? · What is an action receipt? · Glossary