Large language model agents now answer support tickets, query databases and plan trips. When they go wrong, they usually return a confident answer and a success code. A growing body of research explains why these failures are hard to see, and how engineering teams are learning to measure them. A Failure That Looks Like Success Traditional software tends to fail loudly. A function throws an exception, a server returns an error code, and a monitoring dashboard turns red. An AI agent behaves…