Your agent passes every test, then makes 3x more API calls than it needs in production. Binary pass/fail metrics only check the final answer, never how the agent got there. Research shows they miss 73% of quality gradations (Grading Scale, Jan 2026). Two fixes close the gap. LLM-as-Judge gives continuous scores from 0.0 to 1.0 with explanations. You'll see why vague prompts ("is this good?") cause position and verbosity bias, and how explicit rubric criteria keep scores consistent at scale. Trajectory evaluation scores the path, not just the answer, catching duplicate tool calls, irrelevant actions, and unsafe steps. AgentDrift (March 2026) found trajectory evaluation detects 91.3% of issues versus 26.4% for output-only. You'll walk away with: • Continuous quality scoring with explicit rubrics • Automatic trajectory capture via lifecycle hooks • A combined pattern wired into cloud observability Production-ready code, grounded in 2026 research.