Agent Evaluation
A methodology for systematically evaluating the execution chain of an AI agent, including tool calls, reasoning steps, and final output, as distinct from large model evaluation based on a single output.
Timeline (last 90 days)
Rigorous agent research requires matching computational budgets, or at least reporting call counts and latency, to avoid computational confounding.
The instructor repeatedly emphasized in the project: no matter how many layers of evaluation are done, an agent can probably reach 99.9%, but it can never truly reach 100%.
Agent evaluation must simultaneously satisfy three conditions—reasonable result, reasonable evidence, and a reasonable tool-calling chain—before an answer can be judged correct, and none of the three can be missing.
Evaluation is not an additional step after agent development is complete, but infrastructure that runs through the entire development process.
Large model evaluation, RAG evaluation and agent evaluation are three problems at different levels and cannot be handled with the same set of methodologies.
Agent evaluation typically covers dimensions such as Faithfulness, Answer Relevancy, harmful content filtering, and Trajectory Correctness for multi-step tasks.
Agent evaluation is widely recognized in the industry as a difficult problem, because the behavioral space of AI agents is complex, there may be multiple correct paths, and they are difficult to measure with a single metric.
James Gung's research focuses on agent evaluation, dialogue simulation, and proactive agents.
All Facts (8)
Rigorous agent research requires matching computational budgets, or at least reporting call counts and latency, to avoid computational confounding.
50%UnverifiedThe instructor repeatedly emphasized in the project: no matter how many layers of evaluation are done, an agent can probably reach 99.9%, but it can never truly reach 100%.
50%UnverifiedAgent evaluation must simultaneously satisfy three conditions—reasonable result, reasonable evidence, and a reasonable tool-calling chain—before an answer can be judged correct, and none of the three can be missing.
50%UnverifiedEvaluation is not an additional step after agent development is complete, but infrastructure that runs through the entire development process.
50%UnverifiedLarge model evaluation, RAG evaluation and agent evaluation are three problems at different levels and cannot be handled with the same set of methodologies.
50%UnverifiedAgent evaluation typically covers dimensions such as Faithfulness, Answer Relevancy, harmful content filtering, and Trajectory Correctness for multi-step tasks.
50%UnverifiedAgent evaluation is widely recognized in the industry as a difficult problem, because the behavioral space of AI agents is complex, there may be multiple correct paths, and they are difficult to measure with a single metric.
50%UnverifiedJames Gung's research focuses on agent evaluation, dialogue simulation, and proactive agents.
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