Your AI agent passes all tests but makes 3x more API calls than necessary in production. Binary pass/fail metrics only check final answers, missing how agents reach them. Research shows binary metrics miss 73% of quality gradations (Grading Scale, Jan 2026). LLM-as-Judge evaluation provides continuous quality scores (0.0-1.0) with explanations. The Autorubric framework (March 2026) shows explicit rubrics with defined thresholds produce consistent evaluation at scale. You'll learn why vague prompts cause position and verbosity bias, and how to write rubrics with explicit criteria (0.8-1.0 = excellent, 0.5-0.7 = adequate). SCOPE paper (Feb 2026) adds statistical rigor through conformal prediction for finite-sample guarantees. Trajectory evaluation scores the step-by-step path agents take, not just final answers. TRACE framework (Feb 2026) detects duplicate tool calls, irrelevant actions, and unsafe intermediate steps that output-only evaluation misses. You'll capture trajectories automatically using lifecycle hooks, then score for efficiency, relevance, and logical ordering. AgentDrift research (March 2026) shows trajectory evaluation detects 91.3% of issues vs 26.4% for output-only scoring. D3-Gym benchmark (April 2026) validates these approaches: 87.5% agreement between automated LLM-judge evaluation and human annotation when rubrics are well-defined. Walk away with continuous quality scoring, automatic trajectory capture, and integration with cloud observability platforms.