Agentic AI Systems

Agents are the frontier of applied AI in 2026 — and the area with the widest gap between flashy demos and systems that actually work in production. Agentic AI Systems closes that gap. It is the advanced builder course for engineers who can already build production AI and now want to design autonomous, multi-step, multi-agent systems that hold up under real conditions. You will master the full agentic stack as it exists in 2026: agent architecture patterns (ReAct, planning, reflection, tool-use, human-in-the-loop), multi-agent orchestration across the current frameworks (LangGraph for stateful graph workflows, CrewAI for role-based crews, the Claude Agent SDK and OpenAI Agents SDK for vendor-native agents, and the Microsoft Agent Framework that now consolidates AutoGen and Semantic Kernel), and the open protocols that connect them — MCP for tools and A2A for agent-to-agent communication, both now open standards. Crucially, you will go beyond 2023-era patterns. You'll build modern agentic RAG (Self-RAG, Graph RAG, Adaptive RAG), manage agent memory, design tools with least-privilege safety, and evaluate agents properly — trajectory scoring, not just final-answer checks. And you'll confront the uncomfortable production reality head-on: independent research shows a large gap between benchmark performance and real deployment, and no agent framework governs risky actions on its own. You'll learn why demos break at scale and how to build agents that don't. Through hands-on projects and a comprehensive capstone building a complete multi-agent system with trajectory evaluation, cost tracking, and human-in-the-loop checkpoints, you'll graduate ready for Agentic AI Engineer and AI Agent Developer roles — among the highest-paid and most sought-after in 2026. Our Human Intelligence approach ensures you develop the architectural judgment to decide when an agent is the right answer, and when it isn't.