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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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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?→
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