Design Reliable AI Agent Workflows! Learn the architecture behind autonomous AI agents—from core loop mechanics to scalable, production-ready systems.
Building reliable AI agents requires more than writing prompts—it requires designing systems that can plan, execute, evaluate, and continuously improve. Loop Engineering is the structured framework behind these systems, and this course teaches you how it works. You'll start by learning what a Loop is and why it matters. You'll explore how autonomous feedback loops differ from static linear workflows, understand the role of the Scorekeeper in evaluating output quality and detecting drift, and identify the six core components that form every Loop: Automations, Worktrees, Skills, Connectors and Plugins, Sub-agents, and Memory. Next, you'll examine each component in detail. You'll learn how Automations execute tasks, how Git-style Worktrees enable parallel and risk-free testing, how modular Skills and API Connectors expand agent capabilities, how Sub-agents handle task delegation, and how short-term and long-term Memory systems maintain contextual awareness. Finally, you'll bring everything together. You'll trace a complete Loop in motion from initial trigger to Scorekeeper validation, implement error handling and self-correction logic, and design custom loops tailored to specific operational requirements. A capstone role-play scenario challenges you to build an enterprise Loop governance and deployment roadmap. Who this is for: AI engineers, software developers, AI architects, DevOps and automation engineers, and anyone interested in how modern AI agent systems are designed. Prior exposure to AI concepts is helpful but not required.
