Agent Evaluation
Methods for evaluating the correctness of multi-step AI agent tasks, including outcome-based evaluation of the final state, process-based evaluation of the trajectory, and evaluation using an LLM as a judge.
Timeline (last 90 days)
The goal of Agent evaluation is not 100% accuracy, but to bring conclusions and operation results close to 100%, and quantifiable approach targets should be set, such as a key-fact accuracy rate reaching a certain threshold.
Agent evaluation differs from large model evaluation: large model evaluation usually gives a question and looks at the generated answer, whereas an Agent involves multi-step reasoning, tool calls and state transitions, so the object of evaluation changes from a single output to the entire execution trajectory.
Agent evaluation is not equal to software testing: traditional software testing is deterministic judgment (whether functionality works, whether the process goes through), whereas Agent evaluation is probabilistic judgment (whether the answer is credible, whether the reasoning is reasonable, whether the tool selection is correct).
All Facts (3)
The goal of Agent evaluation is not 100% accuracy, but to bring conclusions and operation results close to 100%, and quantifiable approach targets should be set, such as a key-fact accuracy rate reaching a certain threshold.
50%UnverifiedAgent evaluation differs from large model evaluation: large model evaluation usually gives a question and looks at the generated answer, whereas an Agent involves multi-step reasoning, tool calls and state transitions, so the object of evaluation changes from a single output to the entire execution trajectory.
50%UnverifiedAgent evaluation is not equal to software testing: traditional software testing is deterministic judgment (whether functionality works, whether the process goes through), whereas Agent evaluation is probabilistic judgment (whether the answer is credible, whether the reasoning is reasonable, whether the tool selection is correct).
50%