How do we make AI automation dependable enough for finance or compliance?
Agentic workflow design
Agentic workflow design applies one operating pattern: agent proposes, verifier gates. The AI performs the flexible reasoning or generation; deterministic validators, rules, tests, schemas, and human review decide whether the output is safe to proceed. It is built for finance, data migration, and compliance workflows where “mostly correct” is not good enough — because the model is never the last thing to touch the decision.
Why accuracy is the wrong measure
A model that is right 95% of the time is excellent until it runs a thousand times a day against a ledger. Then it is fifty wrong entries daily, delivered with the same confidence as the correct ones.
The useful question is not how often the system is right. It is what happens on the occasions it is wrong, and whether anything notices before the consequence lands.
How the split works
The work is divided by what each component is reliably good at. Models handle ambiguity, language and synthesis. Deterministic code handles certainty. Neither is asked to do the other's job, and the model is never asked to certify itself.
- The agent proposes
- Reads the ticket, drafts the entry, maps the fields, extracts the values — the judgment across messy inputs.
- The verifier gates
- Schema validation, business rules, reconciliation against a source of truth, range and type checks, tests. No model involved, all of it unit testable.
- Pass proceeds, fail holds
- Passing output continues automatically. Failing output routes to a person with the reason attached, rather than being silently retried or silently shipped.
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.
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
Verifier gates · Deterministic validation · Human-in-the-loop
Can the verifier be another model?
Sometimes, but it is a weaker guarantee and should never be the only gate on a consequential action. A model checking a model shares failure modes with it, and both can be confidently wrong about the same thing. Use model-based evaluation to score quality at scale; use deterministic validation to decide whether something is safe to write.
Does gating slow the workflow down?
No. The checks cost microseconds; the model call they follow costs seconds. Some items now stop instead of completing — that is the feature, not the tax.
Related
Last reviewed · Tayyeb Mahmud, 1AYM