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A four-week, independent review of one document-heavy agent and the experts who check its work: where its answers drift, whether reviewers can catch what it gets wrong, and what it needs before you rely on it.
We don't build or sell the agent we test.
Results in your units, such as engagements, loans or cases, not model runs.
Files, traces and interviews stay on-site.
Whether the agent's traces show what it read and why, and whether a reviewer could check its work in the time they have.
What the agent needs to be tested and monitored, including the context a human reviewer used to supply, written so engineering can act on it.
How the agent's answers compare with what your experts actually decided, and where it disagrees for reasons worth hearing.
Which factors drift, by how much, and how often the drift would change a decision. Stable inputs separated from the judgments that move.
One recommendation with the evidence behind it, ready for your risk, quality or governance sign-off.
Most agents in regulated work don't decide alone. An auditor, analyst or reviewer checks the work before it counts. That check is the control everyone relies on, and it is rarely tested. Reviewers see the agent's answer, not how it got there, and they review under time pressure. When the agent is confidently wrong, review can turn into a signature.
The first problem teams hit is variability: run the same file twice and the judgment calls move. The second is quieter: nobody measures what reviewers miss. Our method comes from testing AI agents used in audit with Big Four firms, where the reviewer's sign-off is the evidence.
Audit firms. QC 1000 takes effect December 15, 2026, and covers the technological resources engagement teams rely on. This review gives you evidence on AI tools that experts sign off on.
US banks. SR 26-2 leaves generative and agentic AI out of model-risk scope. This review gives model risk and governance teams evidence where the guidance stops.
Teams operating in the EU. AI used in credit scoring and other listed areas is high-risk under the AI Act from December 2027. Start building the evidence now.
Pick the agent and workflow. Walk through who reviews what, what they see, and how long they have.
From you: the agent's owner and two or three reviewers.
Repeated runs on the same past files, plus a review of the traces.
From you: 10–20 past files with their final decisions, and access to run the agent and see its traces.
Where the answers drift, how they compare with past decisions, and whether a reviewer could check the agent's work in the time they have. Midpoint readout.
From you: 30–45 minutes with each reviewer.
Verify, fix, constrain or retire. Gap register. Final readout.
Findings feed straight into your AI governance. We work with governance and risk teams to turn them into review policy: which decisions stay with experts, what reviewers must see, and when an agent's output can't be relied on.
A repeatable test set built from your own files. Rerun it on every new version of the agent, with or without us, and apply the same method to the next agent.
Building or tuning the agent, production data extraction, platform integration. Reins AI retains its methods and Simthetic technology.
Read the essay: A score is not a judgment