Ai Agents Eval Techniques

AI Agent Evaluation Techniques

[](https://opensource.org/licenses/MIT) [](https://www.python.org/downloads/) [](https://www.langchain.com/) [](https://smith.langchain.com/) [](https://medium.com/@fareedkhandev/implementing-12-ai-agent-evaluation-techniques-using-langsmith-507d5bf5c0aa)

This repository provides a comprehensive, hands-on guide to 12 different techniques for evaluating AI Agents and Retrieval-Augmented Generation (RAG) systems. Each technique is implemented in a runnable Jupyter Notebook, demonstrating practical application using industry-standard tools like LangChain and LangSmith.

For Step by Step explanation of all the techniques, check out the [Medium article](https://medium.com/@fareedkhandev/implementing-12-ai-agent-evaluation-techniques-using-langsmith-507d5bf5c0aa).

Evaluating LLM-powered systems is notoriously difficult. Unlike traditional software with deterministic outputs, the performance of AI agents can be nuanced and hard to measure. Key challenges include:

• Unstructured Outputs: How do you score a free-form text answer that can be phrased in many correct ways?
• Multi-Step Reasoning: How do you evaluate an agent's decision-making process, not just its final answer?
• Dynamic Data: How do you test a system whose "correct" answers change over time?
• Subjective Quality: How do you measure qualitative aspects like "helpfulness," "conciseness," or "faithfulness" to a source?

This repository tackles these challenges by providing clear, practical examples of modern evaluation strategies.

🧪 Table of Evaluation Techniques