Agentic AI Development: Building Autonomous LLM Workflows

The shift from simple chatbots to autonomous agents is transforming how we interact with technology. Understanding how to build systems that can plan and execute tasks independently is the next frontier for developers and innovators. This course guides you through the architecture of agentic systems, moving beyond basic prompts to complex, multi-step workflows. You will learn how to give Large Language Models (LLMs) the ability to use external tools, manage memory, and collaborate with other agents to solve real-world problems. What you'll learn: - Understand the fundamental architecture of autonomous AI agents and their core components. - Design multi-step reasoning workflows using modern LLM orchestration techniques. - Implement tool-calling capabilities to allow agents to interact with external APIs and data. - Apply Retrieval-Augmented Generation (RAG) patterns to ground agent actions in specific knowledge. - Master prompt engineering strategies for reliable agent planning and execution. - Explore ethical considerations and safety guardrails for autonomous systems. Starting with foundational definitions and agent types, the material progresses through memory management, tool integration, and the design of multi-agent environments. You will study clear explanations and code-based implementations to understand how these systems operate in production. This course is designed for beginners interested in the future of AI automation. No prior experience with agentic frameworks or advanced AI development is required. Begin building the next generation of autonomous intelligence today.