Agent Trajectory Evaluation: LLM-as-Judge vs. Jev-as-Judge
Evaluating AI Agent trajectories with two complementary approaches
Why Trajectory Testing Matters
When an AI agent handles a task, it doesn’t just produce one output. It takes a series of actions: calling tools, reading results, and deciding what to do next. This sequence is called the trajectory.
Testing only the final answer misses a lot. An agent can arrive at the right answer through a risky or inefficient path, skipping a verification step, calling the same tool twice, or misreporting a tool result. Trajectory testing asks the more useful question: “Did the agent behave correctly along the way?”
Trajectories can be evaluated in several ways: deterministic trajectory matching, LLM-as-judge, and newer approaches like Jev-as-judge, among others. This post focuses on two of them, LLM-as-judge and Jev-as-judge, to show how differently they can score the exact same trajectory, with real code examples and real comparison results, using a simple trajectory as...
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