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Core
Agent Evaluation and Trajectory Analysis
Agent evaluation grades the whole execution trace (tool calls, observations, state changes, recovery) instead of the final answer alone, because a right answer can mask a broken process and a wrong answer can trace to one bad step in an otherwise sound run. It combines outcome metrics with process metrics such as tool-selection accuracy and step efficiency. AI, ML, and GenAI engineer interviews test it because scoring agents is harder than scoring RAG, and most teams miss it by checking only 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?→LLM & GenAI FundamentalsHow do you evaluate a multi-turn conversation, not just a single response?→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