Agentic AI System Design- Complete Roadmap

A lot of people think an AI agent is just a large language model wrapped in a chat interface. But when you move from a local demo to a real application, that assumption breaks down completely. A true agent is a production software system that can reason over a goal, retrieve context, maintain state, and trigger real actions through APIs. In this video, I break down Agent AI System Design from a builder's perspective. We cover the exact architecture required to make single and multi-agent systems reliable, cost-aware, and safe enough to connect to real external tools. You will learn how to properly route models to save costs, structure your tool contracts, separate workflow state from long-term memory, and implement critical approval gates. By the end, you will have a practical mental model for building production-grade systems that users can actually rely on. Scale Your AI Engineering Skills: 👉 Mastering Agentic AI Certification Our August cohort now has fewer than 10 seats remaining, and we are currently offering 15% off with the code FLASHSALE15. Register here: https://maven.com/aishwarya-srinivasan/mastering-ai-agents?promoCode=FLASHSALE15 👉 AI for Forward-Deployed Engineers Workshop Our workshop is filling up very quickly. We currently have a limited-time 50% discount, so if you have been wanting to understand one of the fastest-growing roles in AI and learn how to build customer-facing AI solutions, now is the best time to register. Register here: https://maven.com/aishwarya-srinivasan/ai-for-forward-deployed-engineers?promoCode=50OFF CHAPTERS 0:00 The Demo vs. Production Reality 1:49 What is an Agent AI System? 2:12 Single Agent vs. Multi-Agent Systems 3:57 Building Block 1: The Model Layer and Routing 5:39 Building Block 2: Tool Contracts and Boundaries 7:37 Building Block 3: Memory vs. Workflow State 9:57 Building Block 4: Orchestration and Control Flow 12:23 Building Block 5: Trace-Level Evaluations 15:16 Building Block 6: Approval Gates and Policy