What does trajectory evaluation measure that final answer accuracy alone cannot capture?

When you evaluate an agent, the easy thing to check is the **final answer**. Did it return the right number, the right summary, the right fix? That is final answer accuracy, and it only looks at the endpoint. The problem is that the endpoint hides how the agent got there. An agent can stumble onto the right answer through luck, take thirty tool calls when five would do, or pick the wrong tool and then accidentally recover. Final answer accuracy gives all of these full marks. Trajectory evaluation grades the whole path instead. It looks at the sequence of steps the agent took: which tools it called, with what arguments, in what order, and whether each step followed sensibly from the last observation. Three things matter in a trajectory. Efficiency, did it use a reasonable number of steps. Tool use correctness, did it call the right tools with the right inputs. Plan coherence, did each step make sense given what came before. The key idea is that a correct answer from a broken process will not survive the next input. Trajectory evaluation catches that fragility before it ships.

**Trajectory evaluation** scores the agent's path, not just its endpoint. Final answer accuracy asks only one question. Is the last output correct? That misses a lot. An agent can reach the right answer by luck, by a wasteful thirty step detour, or by calling the wrong tool and accidentally recovering. All three score perfectly on endpoint accuracy. None is a process you would trust on the next input. Trajectory evaluation grades three properties of the step sequence. Efficiency is step count versus the minimum needed, plus redundant tool calls. Tool use correctness is the right tool with the right arguments in the right order. Plan coherence asks whether each step follows from the previous observation, with sensible recovery after errors. The distinction matters because agents are sequential decision makers, not single shot predictors. A lucky correct answer is not repeatable. A wasteful trajectory burns tokens and latency even when correct. An incoherent trajectory signals the agent does not understand the task. It will fail on a harder variant. Benchmarks like tau-bench and AgentBench report path-level signals because endpoint accuracy alone overstates how reliable an agent really is.