Building Resilient AI Agents with Loop Workflows

AI agents often fail when faced with unexpected inputs, API timeouts, or ambiguous instructions. To build truly dependable AI systems, you must move beyond simple single-turn prompts and implement robust loop workflows that allow agents to self-correct and retry. This text-based course guides you through the process of designing and implementing resilient agentic architectures that handle real-world unpredictability. By the end of this course, you will know how to structure agent behaviors that iterate until a successful outcome is achieved. You will transition from writing fragile scripts to deploying robust, self-healing multi-agent systems using industry-standard design patterns. What you'll learn: - Understand the core concepts of agentic loops, state persistence, and multi-agent orchestration. - Configure LoopAgents within the Agent Development Kit (ADK) to automate repetitive and iterative tasks. - Implement resilient retry mechanisms and error-handling strategies for external API integrations. - Apply output validation loops to ensure agent results meet strict quality and formatting guardrails. - Design collaborative workflows where multiple specialized agents review and refine each other's work. You will begin by mastering foundational terminology and agent design principles before exploring written code patterns for state tracking, loop execution, and system debugging. This course is designed for software developers and AI enthusiasts looking to build production-grade agent systems, with no advanced machine learning background required. Start reading today to build AI workflows that run reliably every time.