Agentic AI Evaluation Framework

Production-ready evaluation framework for agentic AI systems using LangGraph orchestration and LangSmith tracing. Covers retrieval metrics (Hit Rate@K, MRR, NDCG), generation metrics (RAGAS, LLM-as-Judge), and CI/CD quality gates.

Overview

A comprehensive evaluation framework for production agentic AI systems, built around LangGraph for orchestration and LangSmith for tracing and observability. This system closes the gap between prototype RAG pipelines and production-ready systems that can be monitored, tested, and continuously improved.

This framework is also the foundation of my Udemy course: “Agentic AI Evaluation: Production-Ready Systems with LangGraph and LangSmith.”

What It Evaluates

Retrieval Metrics

  • Hit Rate@K — fraction of queries where the correct document appears in top-K results
  • MRR (Mean Reciprocal Rank) — measures ranking quality
  • NDCG (Normalized Discounted Cumulative Gain) — position-weighted relevance scoring
  • RAGAS retrieval metrics — context precision and context recall

Generation Metrics

  • RAGAS generation metrics — faithfulness, answer relevancy, answer correctness
  • LLM-as-Judge pipeline — automated quality scoring with bias mitigation
  • Hallucination detection — citation tracking and factual grounding checks

System-Level Metrics

  • End-to-end latency profiling
  • Cost-per-query tracking
  • Embedding drift detection

CI/CD Integration

# GitHub Actions quality gate
- Run retrieval evaluation suite
- Assert Hit Rate@5 > 0.80
- Assert RAGAS Faithfulness > 0.85
- Block promotion if thresholds fail

Tech Stack

LangGraph · LangSmith · RAGAS · LangChain · ChromaDB · Python · GitHub Actions · Anthropic API