How do we make AI automation dependable enough for finance or compliance?
Agentic workflow design
A model that is nearly always right sounds excellent until it runs against your ledger all day. Nearly right is wrong, repeatedly, and delivered with exactly the same confidence as the correct answers. So the model is never the last thing to touch a decision. The agent proposes; deterministic rules, schemas, tests and a human where the risk warrants it decide what proceeds. Finance, data migration and compliance work is built this way because an auditor, not a user, finds the errors.
Why accuracy is the wrong measure
A model that is right 95% of the time produces fifty wrong entries a day at a thousand runs, delivered with the same confidence as the correct ones. The useful question is not how often the system is right, but whether anything notices when it is not.
Each of those wrong entries costs somebody time to find and put right, and our guide to working out AI ROI shows how to price an error rate into the business case before anything is built.
The pattern that answers it is set out in full on the concept page, with worked detail on what a verifier actually checks.
Volume changes the bill as well as the error count. At a thousand runs a day, each run's tokens and running time add up to a line someone has to own, and our guide to setting budgets and limits on AI spend covers capping it per run and per month.
The agent proposes
The judgment across messy inputs.
The verifier gates
Deterministic checks, none of them a model.
Pass proceeds, fail holds
A failure routes to a person with the reason attached.
Where it earns its keep
Finance-critical automation, data migration validation, knowledge extraction, semantic-layer query validation, report generation, operational triage, and internal tooling that writes to systems of record.
It is unnecessary where the output is read by the person who asked for it before anything happens: a drafting assistant does not need a gate, because the reader is one.
Where one of these processes already runs on RPA bots, our comparison of RPA and AI automation sets out when to keep a bot, add an AI step to it or replace it.
Whether a workflow needs an agent at all is the question to settle first. Our guide to AI agents for business sets out where agents fit, where they do not, and the controls to set before one goes live.
If the process itself is not chosen yet, our guide to which processes to automate first scores the candidates before either is built.
Coding agents run on the same pattern, where tests and a reviewer's approval decide whether an agent's pull request merges. Our comparison of Claude Code and GitHub Copilot covers how each tool's pull requests reach that review.
For a company that reports to the SEC, finance work adds one more test: the gates have to satisfy its auditor under Sarbanes-Oxley. We set out the SOX controls an AI step in the financial close needs, from change management to review, in a separate guide.
What you get
- Workflow design with explicit gate criteria
- Deterministic validator suite
- Human review routing and exception handling
- Audit trail generated as a by-product of the gates
- Test coverage for the verification layer
Start here if
- Automation touches money, identity, or a regulated process
- A previous automation shipped errors nobody caught
- Reviewers are rubber-stamping a queue that is mostly correct
- The process must be explainable to audit or risk
Frequently asked questions
What do you need from us before gate design can start?
The workflow, and the cost of a silent error in it. Those two facts decide how much of the check can be deterministic and where a person has to stand. Everything else follows from them.
Does this work on a process we have already automated?
Yes, and it is the more common starting point. An automation that already runs has a history of the errors it produced, which is the best specification anyone could write for what the verifier has to catch.