Large language model agents are moving beyond simple chat responses into real business operations, where they call tools and interact directly with application data and services. But as the authors of a new arXiv paper point out, completing a user request is not enough: agents must also follow domain-specific business rules. Violating those rules can lead to costly logic bugs, even when the agent appears to succeed.
The paper, titled "AgenTest: Mining Business Rules to Test LLM Agents for Business Logic Bugs," proposes a testing approach that automatically mines the relevant business rules and uses them to check agent behavior. This shifts the focus from generic functional testing to verifying compliance with the actual constraints that govern a given business domain.
The abstract is brief, so details on the mining mechanism and evaluation are not yet available in the provided text. Still, the core idea is clear: instead of manually writing test cases for every possible rule, AgenTest aims to derive the rules themselves from the domain, making it easier to catch violations that might otherwise slip through.