What does trajectory evaluation add beyond task success rate for LLM agent evaluation?

An LLM **agent** solves a task by taking many steps: it reads a goal, picks a tool, calls it, reads the result, decides the next move, and repeats until done. The simplest way to grade it is task success rate, the fraction of tasks where the final answer is correct. That number is honest but shallow. Trajectory evaluation grades the path, not just the destination. It scores each step along the way: was the right tool chosen, were the arguments valid, did the state change as expected, were there pointless extra steps. The answer is option A. The value is diagnostic. If a hundred tasks fail and you only have success rate, you know the agent is broken but not where. If you score the trajectory and find that 80 percent of failures share a wrong tool choice at step 3, you have found a specific planning bug to fix. The other options describe things trajectory eval does not do. It does not auto-write failure explanations, does not read the model's internal confidence, and averaging step scores is not always more reliable than the binary success number.

**Trajectory evaluation** scores the agent's sequence of actions, not just its final answer. The correct option is A: step-level scoring of tool calls, state transitions, and efficiency. Task success rate is a single binary signal per task. It tells you whether the agent finished, but not where it went wrong. For a multi-step agent that hides everything that matters for debugging. An eight-step task that fails at step five looks identical to one that fails at step one. Trajectory eval opens up the run. It checks step correctness (right tool, right arguments) and state transitions (did the world change as intended). It also flags redundant steps (wasted calls that inflate cost) and recoverable errors (a wrong step the agent later corrected). Aggregated across many runs, this localizes failures. If most failed tasks share an error at the same step, that is a targeted fix. The distractors are wrong because they overclaim. Trajectory eval does not auto-generate explanations, does not read internal confidence, and a continuous average of step scores is not always more reliable than binary success.