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Welcome to this course!Build and understand the foundational components of AI agents including prompts, tools, memory, and logicImplement comprehensive evaluation frameworks across quality, performance, and cost dimensionsMaster practical A/B testing techniques to optimize your AI agent performanceUse industry-standard tools like Patronus, LangSmith and PromptLayer for efficient agent debugging and monitoringCreate production-ready monitoring systems that track agent performance over timeCourse DescriptionAre you building AI agents but unsure if they're performing at their best? This comprehensive course demystifies the art and science of AI agent evaluation, giving you the tools and frameworks to build, test, and optimize your AI systems with confidence.Why Evaluate AI Agents Properly?Building an AI agent is just the first step. Without proper evaluation, you risk:Deploying agents that make costly mistakes or give incorrect informationOverspending on inefficient systems without realizing itMissing critical performance issues that could damage user experienceCreating vulnerabilities through hallucinations, biases, or security gapsThere's a smart way and a dumb way to evaluate AI agents - this course ensures you're doing it the smart way.Course Breakdown:Module 1: Foundational Concepts in AI Evaluation Start with a solid understanding of what AI agents are and how they work. We'll explore the core components - prompts, tools, memory, and logic - that make agents powerful but also challenging to evaluate. You'll build a simple agent from scratch to solidify these concepts.Module 2: Agent Evaluation Metrics & Techniques Dive deep into the three critical dimensions of evaluation: quality, performance, and cost. Learn how to design effective metrics for each dimension and implement logging systems to track them. Master A/B testing techniques to compare different agent configurations systematically.Module 3: Tools & Frameworks for Agent Evaluation Get hands-on experience with industry-standard tools like Patronus, LangSmith, PromptLayer, OpenAI Eval API, and Arize. Learn powerful tracing and debugging techniques to understand your agent's decision paths and detect errors before they impact users. Set up comprehensive monitoring dashboards to track performance over time.Why This Course Stands Out:Practical, Hands-On Approach: Build real systems and implement actual evaluation frameworksFocus on Real-World Applications: Learn techniques used by leading AI teams in production environmentsComprehensive Coverage: Master all three dimensions of evaluation - quality, performance, and costTool-Agnostic Framework: Learn principles that apply regardless of which specific tools you useLatest Industry Practices: Stay current with cutting-edge evaluation techniques from the fieldWho This Course Is For:AI Engineers & Developers building or maintaining LLM-based agentsProduct Managers overseeing AI product developmentTechnical Leaders responsible for AI strategy and implementationData Scientists transitioning into AI agent developmentAnyone who wants to ensure their AI agents deliver quality results efficientlyRequirements:Basic understanding of Python programmingFamiliarity with AI/ML concepts (helpful but not required)Free accounts on evaluation platforms (instructions provided)Don't deploy another AI agent without properly evaluating it. Join this course and master the techniques that separate amateur AI implementations from professional-grade systems that deliver real value.Your Instructor:With extensive experience building and evaluating AI agents in production environments, your instructor brings practical insights and battle-tested techniques to help you avoid common pitfalls and implement best practices from day one.Enroll now and start building AI agents you can trust!

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