Every process has a standard path and a set of cases that leave it. Some exceptions are written into policy. Others are applied by experienced people who know when the standard path does not fit. For a team running the process by hand, that knowledge is enough; for an AI task, an exception that is not recorded in its data, rules or instructions is invisible.
Exceptions an AI task should be checked against:
- policy exceptions with a defined condition, such as a grace period
- customer-specific terms agreed outside the standard contract
- seasonal or regional variations in how the process runs
- cases routed to a person because the standard rule does not apply
How is business exception handling different from exception handling in software?
- Exception handling in software deals with errors in a program: a failed call, a missing file, an invalid input.
- Business exception handling deals with valid cases that follow a different rule. Nothing is broken; the case is simply not standard.
- An AI task can handle every software exception correctly and still get business exceptions wrong, if they were never part of its data or tests.
Where does business exception handling break in AI work?
An agent that triages insurance claims is tested on a sample of typical claims and performs well. In use, it rejects claims filed after the deadline, including those covered by a regulator’s grace period that the claims team applies by hand. The exception was common enough to matter and rare enough to be missing from the sample. Why AI misses business rules and exceptions walks through where exceptions like this live and how to test for them.
Exceptions belong in the evaluation set as their own cases, with enough examples to see whether the task handles them. Where they are too few in the data the task learns from, DTS can fill missing cases and rebalance the distribution, and the result is then checked under the same evaluation conditions; business rule validation checks that results follow the rule once it is recorded.