Multi-Agent AI Systems and Production Workflows with LangSmith

As AI applications transition from simple chatbots to autonomous systems, understanding how to coordinate multiple AI agents and monitor them in production is becoming an essential skill. This comprehensive, text-only course guides you through the foundational concepts of multi-agent architectures, Retrieval-Augmented Generation (RAG), and production-grade observability. You will learn how to design collaborative AI workflows and use LangSmith to trace, evaluate, and optimize your systems. What you'll learn: 1. Understand the foundational principles of agentic workflows and multi-agent collaboration. 2. Build robust Retrieval-Augmented Generation (RAG) systems to connect LLMs with external data. 3. Configure LangSmith to trace execution steps, monitor latency, and debug complex agent interactions. 4. Implement evaluation datasets to systematically test and improve your AI application's performance. 5. Design structured prompts and manage state across multiple cooperating AI agents. 6. Apply modern observability practices to transition your AI prototypes into reliable production systems. We begin with key terminology and the core mechanics of LLM agents before progressing to multi-agent design patterns and practical tracing techniques. Through clear written explanations and structured code walkthroughs, you will gain a strong conceptual and practical command of production AI. This course is designed for software developers, data enthusiasts, and technical learners who are new to agentic workflows. No prior experience with LangSmith or multi-agent design is required. Start reading today to unlock the potential of multi-agent AI and production-grade monitoring.