Building AI Agents with OpenAI and Model Context Protocol

As artificial intelligence evolves, the focus is shifting from static chatbots to autonomous agents that can plan, use tools, and collaborate to solve complex problems. This text-based course guides you through the foundational concepts and practical architectures needed to design and build your own intelligent agent systems. You will transition from writing basic prompts to orchestrating sophisticated, multi-agent workflows that connect securely to external data sources. What you'll learn: - Understand the core architecture of autonomous AI agents, including planning, memory, and tool-calling capabilities. - Apply Chain of Thought (CoT) reasoning and structured outputs to improve agent decision-making. - Configure the Model Context Protocol (MCP) to connect your agents to local and remote data sources. - Design collaborative multi-agent systems where specialized agents work together to complete complex tasks. - Deploy lightweight, scalable agent backends using Cloudflare workers. - Explore fine-tuning strategies to customize model behavior and tone for specific use cases. This course begins with essential terminology and the conceptual mechanics of agentic workflows. You will then progress through structured written explanations, architecture breakdowns, and step-by-step configuration guides to build, test, and refine your agent systems. This course is designed for developers, software engineers, and tech enthusiasts who want to move beyond basic API calls and build autonomous systems. No prior experience with AI agent frameworks is required, though a basic understanding of programming concepts is helpful. Start reading today to build and deploy your first custom AI agent system.