What is agent verification?
Agent verification is proving what an AI agent actually did: which sources it read, which tools it invoked, what it decided and what it changed. Without verification, delegation to agents is faith; with it, it is management.
The receipt is the primitive
A verification-first runtime emits a structured receipt per operation — inputs, tool calls with arguments, outputs, approvals. Review shifts from "do we trust the model?" to "check the record", which is a solvable problem.
Gates before consequences
Verification pairs with control: approval gates pause a run before consequential actions — sending, paying, deploying — so a human signs exactly where policy demands and nowhere else.
Trust compounds
Teams that can audit agent work expand its scope; teams that cannot stay stuck at demos. The receipt loop is why verified operations grow while unverified pilots stall.
Starting a verification practice
It is a process change more than a technology purchase. Decide which actions are consequential enough to gate. Agree what a receipt must contain before anyone reviews one. Review the first weeks of receipts deliberately rather than only when something goes wrong. Then widen the agent’s remit in the specific categories the record shows it has earned. Teams that do this expand steadily; teams that skip it stall at the pilot and blame the model.
Common questions
Is this just logging?
Logs are for debugging by engineers; receipts are structured for review by the accountable human — claims linked to sources, actions to approvals.
Does verification slow agents down?
Recording is cheap. The only deliberate slowness is the approval gate — and that is the point.
What does a verifiable agent system require?
Structured receipts per operation, claims linked to their sources, approval gates before consequential actions, and storage of that record somewhere you control.