AI Agent Architecture and Engineering: Build Autonomous Systems

As organizations seek to automate complex workflows, the demand for developers who can build reliable, autonomous AI agents is growing rapidly. This text-based course guides you from the absolute basics of LLM reasoning to architecting sophisticated multi-agent systems. You will transition from understanding basic prompts to designing goal-oriented AI systems that can plan, use tools, and collaborate. Through clear explanations and structured code walk-throughs, you will gain the skills needed to build production-ready agents that solve real-world problems. What you'll learn: - Understand the foundational concepts of AI agent architecture, planning cycles, and memory management. - Build single-agent systems using reasoning frameworks like ReAct and ReWOO. - Design complex multi-agent workflows and stateful graphs using LangGraph. - Integrate external tools, APIs, and Retrieval-Augmented Generation (RAG) to ground your agents in custom data. - Configure visual automation workflows using n8n to connect agents with everyday applications. - Monitor, debug, and optimize agent performance and token usage using LangSmith. The course begins with essential terminology and the core mechanics of LLM reasoning. From there, you will progress through step-by-step written tutorials, starting with simple tool-use scripts and advancing to multi-agent orchestration and production monitoring. This course is designed for developers, software engineers, and tech enthusiasts who want to enter the field of AI engineering. No prior experience with AI agents is required, though a basic understanding of Python is helpful. Start reading today to build your first autonomous AI agent.