Episode 7 - Evaluating AI Agents Explained | Metrics, Testing & LLM-as-a-Judge

Welcome to Episode 7 of CodeTav Management! 🚀 How do we know whether an AI agent is actually working correctly? In this episode, we explore AI Agent Evaluation and learn how to test, measure, and improve the performance of AI agents in real-world applications. An AI agent must do more than generate answers. It should complete tasks correctly, use the right tools, provide accurate information, and operate safely and reliably. What You Will Learn: What is AI Agent Evaluation? AI chatbot vs AI agent evaluation The four pillars of agent evaluation Important AI agent metrics: task success rate, accuracy, tool call success rate, latency, and cost per task What is LLM-as-a-Judge? How to create an evaluation dataset Practical AI agent evaluation workflow How to identify failures and improve agent performance Popular evaluation tools: LangSmith, DeepEval, Ragas, and OpenTelemetry Best practices for production-ready AI agents Who Should Watch? This video is useful for AI engineers, LLM developers, machine learning enthusiasts, software developers, and anyone preparing for AI engineering interviews. Subscribe to CodeTav Management for more practical tutorials on AI engineering, agentic systems, RAG, LLMs, Java, DSA, Spring Boot, and system design. Stay tuned for the next episode on Agent Memory, short-term memory, and long-term memory. #evaluatingaiagents #aievalution #aiengineering #llm #aiagents #generativeai #llmengineering #langsmith #deepeval #ragas #machinelearning #artificialintelligence #codetavmanagement