Your AI agent worked in the demo, impressed stakeholders, handled test scenarios, and seemed ready for production. Then you deployed it, and the picture changed. Real users experienced wrong tool calls, inconsistent responses, and failure modes nobody anticipated during testing.
The result is a gap between expected agent behavior and actual user experience in production. Agent evaluation introduces challenges that traditional software testing wasn’t designed to handle. Because large language models (LLMs) are non-deterministic, the same user query can produce different tool selections, reasoning paths, and outputs across multiple runs. This means that you must test each scenario repeatedly to understand your agent’s actual behavior patterns. A single test pass tells you what can happen, not what typically happens. Without systematic measurement across these variations, teams are trapped in cycles of manual testing and reactive debugging. This burns through API…
