Agentic AI Evaluation: Production-Ready Systems with LangGraph and LangSmith
A comprehensive Udemy course covering production-grade evaluation of agentic AI and RAG systems — retrieval metrics, generation quality, LLM-as-Judge, CI/CD quality gates, and production monitoring.
Course Overview
This course teaches engineers and data scientists how to build production-ready evaluation pipelines for agentic AI systems — moving beyond “it works in the demo” to measurable, monitored, continuously improving systems.
What You’ll Learn
Track A — Core Curriculum (Modules 1–5)
| Module | Title | Focus |
|---|---|---|
| M1 | Why Evaluation Matters | The evaluation problem, the Evaluation Pyramid, course roadmap |
| M2 | Metrics Demystified | Retrieval + generation metrics explained; LangSmith intro |
| M3 | Building Retrieval Evaluators | Hit Rate@K, MRR, NDCG, RAGAS — hands-on |
| M4 | Generation Quality & LLM-as-Judge | RAGAS generation metrics, automated quality scoring, bias mitigation |
| M5 | CI/CD Quality Gates | GitHub Actions pipelines, quality thresholds, production monitoring |
Track B — Advanced Modules (Modules 6–10)
| Module | Title | Focus |
|---|---|---|
| M6 | Embedding Drift & Re-indexing | Drift detection, citation tracking, re-indexing strategy |
| M7 | Latency & Cost Optimization | Profiling, cost-per-query, tradeoff visualization |
| M8 | Multi-Agent Evaluation | Trajectory evaluation, tool call correctness, consistency testing |
| M9 | Healthcare RAG Case Study | Clinical safety rubric, hallucination caught in production |
| M10 | Capstone Project | Build your own eval framework — 9-deliverable checklist |
Tech Stack
LangGraph · LangSmith · RAGAS · LangChain · ChromaDB · Python · GitHub Actions · Anthropic API
Target Audience
ML engineers, AI architects, and data scientists building RAG or agentic AI systems in production.
Status: In development — launching 2026