An AI agent that acts smoothly is not the same as an agent that understands the subject matter. Before deploying an agent to serve a client in a regulated industry, you must demonstrate that it grasps the nuances of the field—not just mimics its form. In this article, I’ll share how we at Blinqx set up that test, with concrete steps you can apply yourself.
Correct output isn’t always accurate
An agent may provide an answer that appears correct but still misses the mark as soon as the context shifts. This happens especially in non-standard situations: an insurance policy clause with an exception, a court ruling that clarifies a previous rule, or a tax return with a slightly different tax situation. Generic language models derive their knowledge from broad training data and therefore often lack the latest industry regulations, as well as a firm’s specific operating procedures.
What testing method demonstrates whether an agent understands the nuances?
At Blinqx, we build these test sets in collaboration with the client’s subject matter experts: lawyers, accountants, or mortgage advisors who provide borderline cases from their own practice. This results in a test set that isn’t generic but precisely addresses where things go wrong in practice. The question isn’t “Can the agent summarize a contract?” but “Does the agent recognize that clause 4.2 creates an exception to clause 2.1, and can he apply that to a new contract?”
How do you build a domain-specific test set for each sector?
1. First,identify the scenarios in which junior employees make mistakes more often than senior employees. By definition, these are the areas where domain knowledge makes the difference.
2 Turn those cases into scenarios with clear right and wrong answers, and have the agent work through them one by one. For industries such as insurance and mortgages, this works slightly differently than in accounting: in mortgage advising, you primarily test knowledge of regulations that vary by client profile, whereas in accounting, you more often test the interpretation of borderline tax cases. The testing method remains the same; the case studies do not.
3. Furthermore, document not only whether the agent provides the correct answer, but also whether he can demonstrate his reasoning. A correct answer without a clear line of reasoning is insufficient in a regulated sector, because a regulator or auditor will want to be able to see how the agent arrived at his conclusion.
4 Repeat your tests. Testing domain knowledge is not a one-time check before going live. Industry regulations change, customer scenarios evolve, and an agent who passes today may encounter a new exception in six months that wasn’t included in the test set. Therefore, build the test set as a living document, not as a checklist.
When is an agent ready to go live with a client?
An agent is ready as soon as he
- the test suite runs consistently well with non-standard cases,
- and knows when to forward a question to a human instead of guessing on his own.
Platform builders often underestimate this last point: an agent that says “I’m not sure” at the right time is more valuable than one that always provides an answer.
That’s why we deliberately build that uncertainty threshold into the system: below a certain confidence threshold, the agent automatically escalates the issue to a human, along with the case details and the reasoning built up to that point. This prevents an agent from venturing into areas where it hasn’t yet demonstrably mastered the nuances.
You don’t set that threshold just once: As an agent gains experience and successfully handles more cases, you can carefully adjust the threshold, provided you back that up with test results. The reverse is also true: as soon as a sector introduces a new rule, you temporarily lower the threshold until the agent has been tested on those new cases.
What do you do if the agent fails the test?
Analyze the type of case that results in a failure: is it a knowledge gap (the agent lacks information), a reasoning error (the agent has the information but draws the wrong conclusion), or a scope issue (the agent should have probed further)? Each of these three requires a different solution: more training data, a more tightly defined scope, or a lower escalation threshold. Depending on the outcome, you’ll start over with Collecting, Translating, or Documenting.
We also discuss these types of cases on Tech TalQX, the podcast about AI in regulated industries.
Frequently Asked Questions
A language test checks whether the agent writes fluently and clearly. A domain knowledge test checks whether the agent correctly applies the technical nuances of a sector, including exceptions and borderline cases. An agent can pass a language test with flying colors and still fail the domain knowledge test.
There is no set number, but focus on covering the known edge cases rather than on a specific number. Start with the cases where junior employees tend to make mistakes, and expand as you encounter new edge cases in practice.
Both, working together. The platform developer provides the testing methodology and structure, while the subject matter expert at the client’s firm provides real-world case studies from their own practice. Without that real-world input, you’re primarily testing generic knowledge, not the specific situation at the firm.
Yes, provided you build in a feedback loop in which errors encountered in practice are fed back into the test set. Without that loop, an agent remains stuck at the knowledge level of the last training round, while industry regulations and case law are constantly changing.
The biggest risk is an agent who confidently gives the wrong answer in an edge case, without anyone noticing until it causes problems for the customer. In regulated industries, this can lead to incorrect advice with financial or legal consequences, and that undermines trust in AI agents much more quickly than an agent who admits in a timely manner that they’re not sure.
