Rejects by default,
so only what actually passes gets through
Giving an AI agent a job is one decision; trusting its output without a second check is a different, riskier one. A critic and verifier agent sits between a primary agent and whatever it is about to publish, send or act on, checking it against a defined rule set and rejecting by default when something is ambiguous. It is never the same model or prompt as the agent it is checking, on purpose.
The role today
An agent that drafts content, a reply, or a decision is only as trustworthy as whatever checks it before that output goes anywhere real. Without a dedicated check, the most common failure mode is not a dramatic error, it is a plausible-sounding mistake that passes a quick glance and only gets caught once it has already gone out.
The second cost is that when the same model or prompt that generated an output is also asked to check its own work, it tends to agree with itself. The blind spots that produced the mistake are often the same blind spots that would miss it on review.
The third is that without a reject-by-default posture, an ambiguous case tends to get waved through, since approving by default feels like the path of least resistance, right up until an ambiguous case turns out to have mattered.
What the agent takes over
The critic checks another agent’s output against a defined rule set before it reaches a customer, a publish step, or an action with consequences, factual accuracy against a source, tone and brand voice, policy compliance, formatting. It runs on a different model and prompt than the agent it is checking, specifically so it does not share the same blind spots.
Its default posture is to reject anything ambiguous rather than approve it, which means a human reviews more at first than a looser system would require, but what passes through unreviewed is genuinely trustworthy. Every verdict, pass or reject, is logged with the specific reasoning, so patterns in what gets rejected become visible and actionable.
Typical scope: gating output from content, support, and operational agents before it reaches a customer or triggers an action. One of our own pipelines runs five stages, each passing through a critic and a human moderator, and cut model calls per video from 66 to about 3 after a redesign without loosening that check.
What stays with humans
Writing the rule set the critic checks against is collaborative, built from what your team already looks for, not invented independently. Reviewing a rejected item and deciding whether it needed rejecting, or whether the rule itself needs adjusting, stays with a human.
Guards
Reject-by-default on anything ambiguous, approval is the exception that has to be earned, not the assumed outcome. The critic runs on an independent model and prompt from whatever it is checking. Every verdict is logged with its specific reasoning, and rejected items land in a visible queue, never silently discarded or retried without record.
Price and timeline
| Option | Price | What it covers | Timeline |
|---|---|---|---|
| Agency runs it | from $2,000 + support plan | Critic built, tuned and supervised by us, monthly rejection-pattern review | 2 to 3 weeks |
| Full control, handover-ready | from $3,400 | Same critic on your own infrastructure, documented rule set, your team maintains it | 3 to 4 weeks |
Running cost is usually $15 to $60 a month in model usage, depending on volume checked.
Related
See the AI agents service page and development for the surrounding build. Within this group: prompt and model evaluation agent checks versions before launch while this one checks live output, and agent orchestrator and code reviewer agent share the same reject-by-default pattern. For a related one-time setup, see automate agent approval queue and audit log. Real pipeline behind this page: the AI video content pipeline case study, where every stage passes a critic and a human moderator, and the ProBay AI agent team case study.
Trusting an agent’s output without a second, independent check? Get in touch and we will look at what is actually at risk first.
FAQ
How much does a critic and verifier agent cost?
From $2,000 to build a rule set and gate for one agent's output, live in 2 to 3 weeks. Checking output from multiple agents, or a more detailed rule set, usually runs $3,000 to $4,500.
How long before it is checking real output?
2 to 3 weeks: building the rule set from your actual approval criteria and testing it against output you have already reviewed by hand takes most of it, to confirm it agrees with your team's judgment before trusting it on new output.
What exactly does it check?
Whatever your rules define: factual accuracy against a source document, tone and brand voice, compliance with a written policy, formatting requirements. We build the rule set from what your team already checks for manually.
Why use a different model or prompt from the agent it's checking?
A critic built from the same prompt as the agent it is reviewing tends to share the same blind spots. Keeping them independent means a mistake one agent is prone to making is more likely to get caught rather than rubber-stamped.
What happens to something it rejects?
It goes to a queue for a human to review, it does not just disappear or get silently retried without anyone seeing why it failed. Every rejection is logged with the specific reasoning, so your team can see whether the rule needs adjusting.