AI Agent Development with h2oGPTe

AI agents represent the next evolution in automation, moving from simple text generation to proactive systems that can use tools and make decisions. This course provides a structured path to understanding how these systems work and how to build them effectively. You will gain a clear understanding of how large language models serve as the reasoning engine for agents and how to wrap them in architectures that support memory and external actions. By the end of this course, you will be able to design autonomous workflows that can handle complex tasks through structured reasoning and tool use. What you'll learn: - Understand the core architecture of AI agents and how they differ from standard chat models - Master prompt engineering strategies to guide agent reasoning and decision-making - Apply Retrieval-Augmented Generation (RAG) patterns to provide agents with domain-specific data - Configure memory systems to help agents maintain context across long-running tasks - Practice integrating external tools and APIs to allow agents to interact with the real world - Explore modern evaluation patterns to measure agent performance and reliability The course begins with essential terminology and the theoretical foundations of agentic behavior before moving into the practical mechanics of building with h2oGPTe. You will read through detailed explanations and analyze code-based examples to solidify your understanding of agent workflows. This course is designed for beginners interested in AI and automation; no prior experience with AI agents is required. Start building intelligent, autonomous systems today.