AI agent evaluation requires trajectory analysis and real-world dependencies

Effective evaluation of AI agents requires analyzing the full trajectory of actions rather than focusing solely on the final result. While a high score may indicate a successful task completion, it can mask significant inefficiencies, such as unnecessary tool calls, redundant information searches, or excessive token consumption.

To ensure reliability, testing must occur in environments that mirror production. monday.com AI Engineering Director Dor Cohen highlighted that using mocked databases and fake APIs can create a gap between testing and real-world behavior. For instance, an agent might produce a well-structured and seemingly correct answer while failing to process all required data points.

Comprehensive evaluation suites must check the entire chain of tool calls and dependencies to ensure completeness and cost-effectiveness, catching errors that simple result-based grading would overlook.