The pilot proved value in six weeks; production approval is measured in months, because risk has nothing to review.
Pilots stall because risk has no evidence to sign off on. Give every run a record and a verdict, with holds for runs that breach the rules, and the review has something concrete to approve.
The technology question was answered months ago. The approval question is still open, and it is the one that decides whether the agent ever ships.
Teams describe six months per review round, production sign-off taking up to a year, and structured adoption programmes running 12 to 24 months. The build took six weeks; the queue takes the rest.
Production is so far away that a budget reshuffle or a reorganisation kills the POC before the sign-off arrives. The agent did not fail; the calendar did.
What did it access, what happens when it goes wrong, who gets alerted. Each committee asks again, and each time the answers are rebuilt by hand because nothing records them.
SOC 2, ISO 27001, the EU AI Act, and sector regimes each want documentation of what the agent does and how it is controlled. Assembling that manually takes longer than the pilot did.
Risk teams are not being obstructive. They are being asked to approve something that arrives with no evidence and no controls.
A demo shows the agent working once. It says nothing about run 4,000, what the agent will and will not do, or how often it fails. Reviewers cannot sign off on the absence of evidence, so they ask for more time instead.
The reviewer asks what stops a bad run before it acts. If the honest answer is nothing, the review cannot close, because approval would mean accepting whatever the agent does next.
The POC existed to prove value fast, so it kept no record of its own runs. Everything the review needs, what it accessed, how it failed, who was told, has to be retrofitted after the fact.
Compliance controls were written for deterministic systems and human operators. Mapping an agent's behaviour onto them is translation work nobody owns, so it lands on whichever team wants the approval most.
Prefactor watches every run and evaluates the outcome, so the evidence risk keeps asking for accumulates from the first week of the pilot.
Instrument the pilot itself. Native SDKs for common frameworks, a TypeScript and Python core SDK for anything custom, and OpenTelemetry ingest for closed tools. Evidence starts accumulating in week one, not after the review requests it.
Every run in one queryable record. What the agent accessed, decided, and changed, per run, tagged to the agent that did it. "What did it access?" becomes a lookup instead of a reconstruction.
Every run gets a verdict. Each run is checked against the agent's job, so failure rates, failure types, and quality per version are numbers a reviewer can read, not assurances they have to take on trust.
Evidence lines up with the frameworks. The record maps to the controls in SOC 2, ISO 27001, the EU AI Act, and sector regimes, so the review reads reports rather than commissioning an evidence project.
The record answers what the agent did. The controls answer what happens when it goes wrong, which is the other half of every review.
Holds and escalation exist before the review asks. A run that fails its evaluation can be held or routed to a person before it acts. When the reviewer asks what stops a bad run, the answer is a mechanism you can demonstrate, not a plan to build one.
Each review round starts from the last one. The questions risk asked become saved queries against the record, so the next committee opens with answers instead of restarting discovery, and each round gets shorter rather than longer.
The pilot carries its record into review. What it accessed, how often it failed and how each failure was handled, who was alerted and when. Sign-off becomes a review of evidence rather than an act of faith, which is the version risk teams can actually approve.
A pilot proved value in six weeks, then spent two quarters restating the same answers about access and failure handling for each committee, and was nearly cancelled when its sponsor changed roles. Instrumented from the start, the same pilot arrives in review with a per-run record, evaluation results across a few thousand runs, and a hold path the reviewer can watch fire. The committee's questions become lookups, and the open item list shrinks instead of rolling over. Illustrative, but the questions are the standard ones.
The portfolio's pilots are stalled in the same queue for the same missing evidence; one record shortens every review at once.
See the solution → Security & governanceA per-run record, demonstrable holds, and evidence mapped to your frameworks: the material a sign-off can actually rest on.
See the solution → Product leadersA pilot that proved value is a roadmap asset only if it ships before the funding moves; a shorter path to sign-off protects it.
See the solution →Book a demo and we will show what a pilot looks like when every run arrives in review already watched, evaluated, and answerable.
Prefactor helps teams observe, evaluate, and improve their AI agents in production, across every framework and provider.