Multi-Agent Systems and Orchestration with GitHub Workflows

As AI applications evolve from single prompts to complex ecosystems, coordinating multiple specialized agents is becoming the industry standard. Building these systems requires robust orchestration, clear communication protocols, and reliable error recovery. This text-only course guides you through the foundational concepts of multi-agent systems and teaches you how to orchestrate their interactions. You will learn to use GitHub-native workflows as the coordination backbone, ensuring your agents communicate reliably, log their actions clearly, and handle failures gracefully. What you'll learn: - Understand the core principles of multi-agent architecture and task decomposition. - Design coordination patterns using GitHub-native artifacts and environments. - Implement structured logging to maintain deep observability across agent interactions. - Configure automated recovery workflows including retries, rollbacks, and error handling. - Integrate human-in-the-loop escalation patterns for safe and reliable agent execution. - Apply modern state management strategies to prevent data loss during multi-stage runs. The course starts with essential terminology and foundational multi-agent patterns before moving into practical orchestration strategies. You will progress through written explanations and structured code snippets that demonstrate real-world coordination, observability, and error-handling setups. This course is designed for software developers, system architects, and AI enthusiasts who want to build resilient distributed agent workflows. No prior multi-agent experience is required, though a basic familiarity with GitHub and software development concepts is helpful. Begin reading today to master the fundamentals of modern agent orchestration and build reliable, self-healing systems.