Master quality, performance & cost evaluation frameworks for LLM agents using Patronus, LangSmith tools
Welcome to this course!
What you’ll learn
- Explain the core components of AI agents (prompts, tools, memory, and logic) and how they work together to accomplish tasks.
- Build a simple AI agent from scratch using Python and modern AI frameworks.
- Design comprehensive evaluation metrics across quality, performance, and cost dimensions.
- Implement effective logging systems to track agent metrics in real-time.
- Conduct systematic A/B testing to compare different agent configurations.
- Use specialized tools like LangSmith, Patronus, and PromptLayer to trace and debug agent workflows.
- Set up production monitoring dashboards to track agent performance over time.
- Make data-driven optimization decisions based on evaluation insights.
Course Content
- Introduction –> 6 lectures • 27min.
- How to evaluate your Agents? –> 4 lectures • 20min.
- Advanced Evaluation Techniques –> 4 lectures • 18min.
Requirements
Welcome to this course!
- Build and understand the foundational components of AI agents including prompts, tools, memory, and logic
- Implement comprehensive evaluation frameworks across quality, performance, and cost dimensions
- Master practical A/B testing techniques to optimize your AI agent performance
- Use industry-standard tools like Patronus, LangSmith and PromptLayer for efficient agent debugging and monitoring
- Create production-ready monitoring systems that track agent performance over time
Course Description
Are 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 information
- Overspending on inefficient systems without realizing it
- Missing critical performance issues that could damage user experience
- Creating vulnerabilities through hallucinations, biases, or security gaps
There’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 frameworks
- Focus on Real-World Applications: Learn techniques used by leading AI teams in production environments
- Comprehensive Coverage: Master all three dimensions of evaluation – quality, performance, and cost
- Tool-Agnostic Framework: Learn principles that apply regardless of which specific tools you use
- Latest Industry Practices: Stay current with cutting-edge evaluation techniques from the field
Who This Course Is For:
- AI Engineers & Developers building or maintaining LLM-based agents
- Product Managers overseeing AI product development
- Technical Leaders responsible for AI strategy and implementation
- Data Scientists transitioning into AI agent development
- Anyone who wants to ensure their AI agents deliver quality results efficiently
Requirements:
- Basic understanding of Python programming
- Familiarity 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!