Agents produce output faster than anyone can read it, and expert hours become the ceiling on what ships.
Manual review caps how much AI you can ship. Define what good looks like once per agent, evaluate every run against it automatically, and send people only the runs that fail or a sample worth auditing.
The agent was meant to save time. Somewhere along the way, checking its work became a full-time job.
The people who know the domain spend their days reading agent output instead of doing the work only they can do. Their available hours, not the agent, cap how much ships.
Staff re-check everything the agent produces, so the hours the agent saved are spent verifying it. On paper the process is automated; in practice it has one more step than before.
Quality lives in each reviewer's head as a judgement call. Two reviewers disagree on the same output, and because the standard was never written down, nothing about it can be automated.
The aggregate number sits stubbornly under where it needs to be, and nobody can say which runs drag it down, because no one evaluates runs individually. The team tweaks prompts and hopes.
Each of these was a reasonable call at the time. Together they put a person in front of every output.
Building evaluation looked like a project with a budget line. A person reading outputs looked free, so nobody priced the person's hours, and the free option quietly became the most expensive one.
The pilot was judged by feel: the expert read the output and nodded. That worked at ten outputs a week. A judgement that exists only in someone's head cannot be applied by anything but that head.
One team spot-checks, another keeps a spreadsheet, a third trusts complaints to surface problems. The numbers are not comparable, so there is no shared standard to automate against.
Reviewing every output feels safer than sampling, but a reviewer skimming their four-hundredth item of the day catches less than a rested one reading thirty flagged runs. Full coverage by tired eyes is thinner than it looks.
Prefactor turns the checklist in the reviewer's head into criteria the platform applies to every run.
Write down what good means, once per agent. The checks the expert applies by instinct, task completed, format met, policy respected, facts grounded, become evaluation criteria the platform can run. The standard leaves the reviewer's head.
Every run lands in one record. Outputs, tool calls, and decisions are captured as they happen, whoever built the agent, so there is a complete population to evaluate rather than whatever a reviewer happened to open.
Every run gets a verdict, automatically. Each run is checked against its agent's criteria: pass, fail, and why. Coverage goes from the fraction a person could read to all of it, without adding a reviewer.
Only the runs worth an expert reach one. Failures and a sample for audit route to a person; the rest do not. The queue shrinks from everything to the handful where human judgement actually changes the outcome.
Automated verdicts clear the queue. The loop is what moves the accuracy number.
Failed runs wait for a person. A run that fails its evaluation can be held or escalated before its output goes anywhere, so cutting manual review does not mean bad outputs reaching users unchecked.
The plateau becomes a diagnosis. Verdicts segmented by input type, prompt version, and model show exactly which runs fail and what changed the number. The team stops tweaking on instinct and starts fixing named failure classes.
Quality becomes a trend, not a feeling. Each agent carries its pass rate per version over time, measured the same way across teams, so "is it good enough yet" is answered with a chart rather than a meeting.
Two domain experts were reading roughly 400 agent outputs a week between them, and accuracy still sat at 81 per cent against a 95 per cent target. Writing their checklist down as evaluation criteria and running it on every run cut human review to failures plus one run in twenty, about 30 reads a week, and showed that most failures came from one input type the prompt had never handled. One prompt change moved the number more than six months of full manual review had. Illustrative, but this is the usual shape.
Stop lending your best engineers to the review queue; define criteria once and let evaluation run on every run they ship.
See the solution → Product leadersThroughput capped by reviewer hours is a roadmap problem; automated verdicts take the cap off without taking quality with it.
See the solution → Heads of AIOne definition of good per agent and one quality trend across teams, instead of six spreadsheets that cannot be compared.
See the solution →Book a demo and we will take one agent you check by hand, define its criteria, and show every run evaluated against them.
Prefactor helps teams observe, evaluate, and improve their AI agents in production, across every framework and provider.