Course Description

This course is designed to help developers, AI engineers, and technical product teams understand and implement autonomous, agentic AI workflows. Through hands-on modules and real-world scenarios, learners will explore key components like planning, memory, and tool integration, while mastering design patterns and architectural best practices for building robust, modular AI agents. The course also covers evaluation frameworks and deployment strategies, equipping participants with the skills to take agentic systems from prototype to production. This course provides the foundation to create scalable, intelligent agentic AI solutions.

Course curriculum

  • 1
    Introduction to AI Agent Operations
    • Course Introduction
    • Course Handouts
    • Introduction to AI Agents and Agent Ops
    • Overview of LangGraph
    • Overview of Arize Phoenix
    • Setting up your Environment for Agent Ops
    • Importance of Agent Ops (Agentic AI Operations)
    • Quiz
  • 2
    Building an Agentic AI System
    • Defining the Agentic AI System Objective
    • Build an Agentic AI System with LangGraph
    • Hands On: Build a Simple Agentic AI System with LangGraph
    • Quiz
  • 3
    Build an API for your Agentic AI System
    • Basics of API and Web Service Design
    • Wrapping Agentic AI as an API using FastAPI or Flask
    • Hands On: Wrapping our Agentic AI System as an API using FastAPI
    • Testing your API
    • Security Considerations for Web Service APIs
    • Quiz
  • 4
    Deploying your AI Agent
    • Local vs Cloud Deployment Options for AI Agents
    • Recap of our Agentic AI System and API
    • Setup Cloud Environment & Deploy our Agent API
    • Best Practices & Advanced Deployment Strategies
    • Quiz
  • 5
    Testing and Monitoring your AI Agent
    • Monitoring and Tracing Agent Executions
    • Prompt Testing and Iteration
    • Implementing Safe and Responsible AI
    • Quiz
  • 6
    Course Wrap-up and Next Steps
    • Summary of Key Learnings
    • Course Conclusion and Future Scope

Who Should Enroll?

  • AI/ML engineers building autonomous or LLM-based systems

  • Analysts and associates looking to accelerate research and outputs

  • Technical product managers designing AI-driven workflows

  • Researchers and tinkerers interested in agentic architectures

Key Takeaways

  • Learn to design modular, scalable agentic AI systems

  • Implement planning, memory, and tool-use in agents

  • Apply design patterns and best practices for agent architecture

  • Evaluate and deploy production-ready AI agent workflows

Course Instructor

John Gilhuly - Head of Developer Relations @ Arize AI

John Gilhuly is a seasoned data science and AI consultant with deep expertise in building ML systems and deploying end-to-end AI solutions. With a strong background in Python, MLOps, and agentic AI architectures, John has worked across industries to drive innovation through advanced analytics and large language models. He's passionate about bridging the gap between cutting-edge AI and real-world impact, and brings a practical, hands-on approach to teaching and mentoring aspiring AI practitioners.
Course Instructor

Course Instructor

Srilakshmi Chavali - AI Engineer @ Arize AI || UC Berkeley Alumna

Srilakshmi Chavali is a UC Berkeley alum with a double major in Computer Science and Cognitive Science. She’s passionate about building real-world ML/AI solutions and brings hands-on experience in Python, data analysis, and algorithm development. With a strong foundation and a drive to stay on the cutting edge, Srilakshmi is excited to help learners dive into the world of machine learning.
Course Instructor

FAQ

  • Do I need prior experience with LangChain or agent frameworks?

    No prior experience is required. The course starts with the fundamentals and progressively builds toward advanced agentic system design.

  • Is this course suitable for beginners in AI?

    While the course is beginner-friendly in structure, it’s best suited for learners with some familiarity with Python and foundational AI/ML concepts.

  • Will I get hands-on experience building AI agents?

    Yes! The course includes practical projects and guided labs where you'll build and deploy your own agentic AI systems using modular components.

  • What tools and frameworks are covered in the course?

    The course explores key agentic design patterns and tools like LangChain, OpenAI APIs, memory modules, planning agents, and evaluation frameworks.

  • Can I apply these skills in production environments?

    Yes, participants will receive a certificate after finishing the course.