May 23
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Identical LLMs under the hood. Completely different systems built around them. Most AI engineers don't realize how far consumer and enterprise agent architectures have diverged — until they've already built the wrong one. In this video, I break down: • Why the LLM is the easy part — memory, orchestration, and governance are what actually define enterprise agents • Consumer failure = annoying. Enterprise failure = financially and legally risky. That gap reshapes every architectural decision • Single-agent (consumer) vs multi-agent pipelines (enterprise) — when the added complexity pays off • Enterprise memory layers: working, episodic, semantic, procedural, and organizational — each serving a distinct purpose • The Agent OS layer: how LangGraph, CrewAI, AutoGen, and the OpenAI Agents SDK are becoming the runtime for enterprise agents • Why enterprise AI converges with distributed systems, DevOps, and security engineering — not just LLM prompting • The convergence trend: where consumer usability and enterprise reliability are heading next If you're an engineer or architect deciding how to build production AI agents, this is the technical breakdown that most "intro to AI agents" videos skip entirely. ───────────────────────────────────────────────────────────────────── 🕐 Chapters 0:00 – The industry lumps all agents together — here's why that's wrong 0:33 – Same foundation, different goals 1:11 – Different definitions of success 1:53 – Consumer agent architecture 2:36 – Enterprise agent architecture 3:17 – Memory system differences 3:57 – Enterprise memory layers explained 4:42 – Reliability expectations and failure tolerance 5:39 – Security and governance 6:24 – Single-agent vs multi-agent 7:10 – Agent orchestration: LangGraph, CrewAI, AutoGen, OpenAI Agents SDK 7:58 – UX differences: chat interfaces vs audit dashboards 8:50 – Autonomy models: reactive vs always-on 9:35 – How economics drive architecture decisions 10:25 – Why enterprise AI feels harder to build 11:15 – Convergence: where both worlds are heading 12:06 – Developer takeaways: what to actually prioritize 12:56 – Summary ───────────────────────────────────────────────────────────────────── #AIAgents #EnterpriseAI #LLM #MultiAgentSystems #AgentOrchestration #LangGraph #CrewAI #AutoGen #AIArchitecture #AgenticAI #AIEngineering #MachineLearning #OpenAI #AIInfrastructure #SoftwareArchitecture #claudecode #hermesagent