Building Production AI Agents using Python and Multi-Agent Frameworks

The complexity of modern AI applications requires sophisticated orchestration. Ready to move beyond simple prompts and build intelligent systems that can automate complex, enterprise-level tasks? This course provides a practical, code-focused foundation in designing, building, and deploying AI agents that are reliable, traceable, and scalable. You will transition from basic agent concepts to implementing enterprise-grade multi-agent architectures using leading Python frameworks. What you'll learn: * Understand the core concepts of LLM agents, including the ReAct pattern and effective tool utilization (function calling). * Design and implement complex multi-agent workflows using modern orchestration frameworks like LangGraph and AutoGen. * Apply Retrieval-Augmented Generation (RAG) techniques to ground agents in proprietary data using vector databases. * Practice essential LLMOps principles, including monitoring, tracing, and basic evaluation metrics for agent performance. * Configure robust deployment pipelines for agent systems, integrating concepts like CI/CD fundamentals and containerization basics. The course begins by defining key terminology and foundational agent patterns before diving into hands-on implementation of multi-agent architectures. We focus purely on written explanations and practical code examples to illustrate production readiness. This course is designed for beginner developers and software engineers who have a working knowledge of Python and want to specialize in building deployable AI agent systems. No prior experience with LangChain, LLMOps, or advanced AI is required. Start mastering the architectures required for the next generation of AI applications today.