LLM-as-a-Judge

Prerequisite: This is the starting part of the series — no prior part is required. Later parts assume the concepts introduced here. Answer-first: Production multi-agent evaluation requires multi-dimensional grading rubrics, LLM-as-a-Judge harnesses, and trace trajectory analysis. Evaluating task completion, tool call accuracy, and path efficiency in Go benchmark pipelines prevents behavioral drift and ensures deterministic reliability. Implementing this architecture enforces sub-50ms P99 latency guarantees, strict component isolation, and automated observability pipelines required for production-grade enterprise operations. ...

Prerequisite: Familiarity with the concepts introduced in Part 9 — Agentic Observability Monitoring. Review it first if the terminology in this part is unfamiliar. Part 10 — Production Evals & CI/CD Guardrails: LLM-as-a-Judge at Scale In traditional software development, continuous integration (CI) relies on deterministic unit and integration tests. A function either returns the expected string or it fails the build. In GenAI and RAG engineering, responses are non-deterministic. A minor adjustment to a system prompt, a change in vector embedding models, or an update to chunking strategy can silently degrade response quality, introducing subtle hallucinations or dropping key context facts. ...