A study in "Agent Trajectory Evaluation": judging an AI agent's full Execution Path, Not just its final Answer. A small ReAct Agent generates Trajectories; ~40 are hand-labelled as Ground truth; 'Rule-based and LLM-judge scorers' are measured against 'those labels'. The Deliverable is the agreement analysis, and where Automated Evaluation breaks.
python trajectory-analysis inter-rater-agreement failure-analysis ground-truth-labels ai-engineering llm-evaluation react-agent llm-as-judge agentic-workflow agentic-ai agent-memory agent-evaluation inter-rater-reliability eval-harness
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Updated
Aug 25, 2026 - Python