Draw This Before You Build an AI Agent

Before you write a single line of LangGraph, CrewAI, or Python code, there is one control flow graph you must draw as part of your system design. In this video, I share a 5-question framework to help you architect reliable agents, protect irreversible actions, and decide whether you need an autonomous agent, a state machine, or a simple deterministic workflow without AI. Most Agentic AI failures in production are caused by architectural issues like unconstrained loops, missing state persistence, non-deterministic tool calling, and lack of human-in-the-loop gates. Before writing code, map the control flow as a visual graph. Here, I break down the end-to-end system design blueprint for building production-grade AI agents. This is an essential practice for Software Architects, Forward Deployed Engineers (FDEs), AI Engineers, PMs, Data Scientists, and anyone preparing for AI System Design interviews. Get ready with a pen and notepad. CHAPTERS 00:00 - Control flow as a graph 01:15 - The 5 Questions to Answer Before Coding 03:10 - Control Flow vs. Model Autonomy 05:45 - Handling Irreversible Tool Actions & Human-in-the-Loop 08:20 - Real-World Case Study: Production Agent Architecture 10:30 - Key Takeaways for AI System Design & Interviews 🔗Other Architecture Deep Dives: • Stop Building Unreliable AI Agents. Use State Machines:    • Stop Building Unreliable AI Agents. Use St...   • Multi-Agent Architecture: The Stateless vs. Stateful Decision:    • Multi-Agent Architecture: The Stateless vs...   #AIAgents #SystemDesign #SoftwareArchitecture #LangGraph #AIEngineering #MachineLearning #TechInterview