How an AI agent verifies its own work before sending it

The first draft is rarely the finished job

MindStudio's engineering blog puts a first-pass AI agent output at roughly 60 to 70 percent finished. The rest comes from manual back-and-forth: a human gives feedback, the agent fixes one thing, the human gives more feedback again, and the total climbs toward 90 or 95 percent only after several rounds. That back-and-forth is the real cost of running agents without a check built in, not a bad first attempt, but the hours spent reviewing it by hand.

MindStudio frames the fix as a question, not a better prompt: if a person reviewed this work before approving it, what would they actually do? They might click through it the way a user would, or compare it against an example they already trust. An agent can usually run a version of that same manual review itself, before a human ever opens the file.

What the check does, in order

In practice the sequence is short. The agent produces a draft, a reply, a change to a record. Then it runs a defined check against that output: a screenshot compared to the previous version, or a score against a small set of known-good examples. MindStudio calls this an AI eval when the grading is code-based, and an LLM-as-judge when the correctness call needs reasoning rather than a fixed rule. Either way, the output does not move forward until the check returns a pass.

Munder Difflin's engineering blog adds a sharper version of the same rule: every factual claim in an agent's report should cost a command. "The inbox is cleared" should mean the agent queried the count and got zero, not that it wrote the sentence "inbox cleared." "Only two records changed" should come with the diff attached, not a memory of what was touched. A sentence with no command behind it is, in Munder Difflin's words, a guess wearing a lab coat.

There is a second trap inside that same discipline: a check can run and still prove nothing, because it ran under conditions nobody else can reproduce. Munder Difflin describes an agent working in a fresh copy of a codebase that reports a passing build, when the fresh copy was missing a dependency the build needed just to start. The lesson carries past code: a check only counts if it happens under the same conditions the real action will run under, not a shortcut version of them. What audit trails an AI agent should leave behind is largely a record of which conditions a given check actually ran under.

Some output is easy to check. Some isn't.

Digital Applied's research blog ranks output types on what it calls a verifiability ladder, and the ranking has nothing to do with how capable the model is. Text and web pages sit at the readable end, because an agent can read the markup and the console output, then compare that against a screenshot of the same result it just produced. A 3D scene, a video, or a physical action sits further up the ladder, where the agent can no longer fully read what it made, and the loop stops closing on its own.

The same ladder shows up in ordinary business automation. An agent that drafts a reply or updates a record is working at the readable end: it can re-read its own draft against the original request and the account history, the same way it would check a web page against its markup. An agent judging tone or handling a live phone call is working further up the ladder, where the honest answer is that a person is still the checker.

Where a person stays in the loop

Munder Difflin's recommendation for anything that actually ships is to have a second, independent agent re-verify the first one's work, rather than trust the agent that did the work to also grade it. That matters more as tool access grows: MindStudio points out that an agent which can technically send an email will eventually send one, so what gets verified has to include what the agent is allowed to touch, not only what it produces. How to set limits on what an AI agent can do alone and what happens when an autonomous agent makes a mistake both come down to the same design question: what is this agent allowed to do before anyone checks it?

We run this same discipline on our own operation: 197 scheduled jobs across 78 live integrations. Each one has to decide on its own whether what it just did was correct before it moves to the next step. Some of those checks close on their own: text against a rule, a number against a source record. Others get flagged for a person, because the honest answer is that no automatic check closes that loop yet.

Pick one task in your business that currently gets marked done on a person's or an agent's word alone, with no record attached to it. Ask what single piece of evidence, a screenshot or a timestamped log, would have to exist for you to believe it without checking it yourself. If nothing like that exists today, that is the gap worth closing first.