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Core
Agent Evaluation and Trajectory Analysis
Agent evaluation scores the full execution trace (tool calls, observations, state changes, recovery) rather than only the final answer, because a correct answer can hide a broken process and a wrong answer can come from one bad step in an otherwise sound run. It pairs outcome metrics with process metrics like tool-selection accuracy and step efficiency. Applied AI interviews probe it because grading agents is harder than grading RAG, and most teams get it wrong by only checking the last message.
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TAUGHT IN THESE COURSES
This concept is a reference. These lessons put it in sequence, with what comes before and after it.
Agent Engineering·Evaluating agentsPremium13mWhy scoring the final answer tells you almost nothingAn agent that reaches the right answer through eight wrong turns scores identically to one that went straight there, and the two are not the same system. This lesson is what to look at instead, and why it changes which problems you can fix.Agent Engineering·Evaluating agentsPremium14mThe measures worth keepingSix measures cover almost everything useful about an agent, and four of them need no judgement at all. This lesson is what each one tells you, which failure it points at, and the order to build them in.Agent Engineering·Evaluating agentsPremium13mEvaluating a conversation, not a requestOnce an agent talks to a person across several turns, every method so far breaks, because there is no fixed input to replay. This lesson is how to evaluate something whose input depends on its own previous output.
RELATED CONCEPTS
PRACTICE THIS IN REAL QUESTIONS
RAG & Agent System DesignHow do you evaluate an AI agent, beyond just checking the final answer?→RAG & Agent System DesignHow do you evaluate an agent's trajectory and tool-use accuracy, not just its final answer?→RAG & Agent System DesignYour single-turn evals pass but the agent falls apart by turn six. How do you catch that before launch?→RAG & Agent System DesignWhen do you build an agent instead of a single LLM call, and how do you keep a multi-step agent reliable?→RAG & Agent System DesignHow do you evaluate a RAG system end to end when you have no single ground-truth answer?→RAG & Agent System DesignWhen do you use a multi-agent system, and what orchestration patterns and pitfalls matter?→
COMPANIES THAT ASSUME THIS