Evaluating AI Agents

Master quality, performance & cost evaluation frameworks for LLM agents using Patronus, LangSmith tools

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

Requirements
- Basic understanding of Python programming
- Familiarity with AI/ML concepts is helpful but not required
- No prior experience with AI agents is necessary - we'll cover the fundamentals

Description
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 EvaluationStart 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 & TechniquesDive 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 EvaluationGet 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 & Developersbuilding or maintaining LLM-based agents

Product Managersoverseeing AI product development

Technical Leadersresponsible for AI strategy and implementation

Data Scientiststransitioning into AI agent development

Anyonewho 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!

Who this course is for:
- AI developers and engineers looking to build more reliable and cost-effective agent systems
- Product managers overseeing AI initiatives who need to evaluate ROI and performance
- Business leaders making decisions about AI investments and implementations
- Technical professionals transitioning into AI roles who want to understand best practices for agent evaluation
More Info