Here’s some thinking about AI Agents that might be useful. I've been noodling on AI agent architecture, and this framework cuts through the typical hand-waving about "intelligent systems." The core insight? AI agents are graphs, not some linear conveyor belt of logic. Think about it - traditional workflows are for accountants and middle managers. Real problem-solving involves cycles, backtracking, non-deterministic behavior. Once you start thinking in graph structures, you can actually modularize this mess. The seven node types that matter: 🧠 **LLM Nodes** - Your reasoning engine (when it's not hallucinating) 🛠️ **Tool Nodes** - Actually DO something (APIs, databases, web scraping) ⚙️ **Control Nodes** - Logic gates and routing (the boring but essential stuff) 📚 **Memory Nodes** - Context retention, because goldfish memory kills agents 🚧 **Guardrail Nodes** - Safety checks (before your agent starts ordering plutonium) 🔄 **Fallback Nodes** - Shit breaks. Plan for it. 👥 **User Input Nodes** - Humans in the loop (revolutionary concept, I know) It's modular Lego blocks for problem solving and iteration. The graph approach lets you spot failure points before they manifest, balance automation with human oversight, and - this is key - actually understand what your system is doing at each step instead of praying to the LLM gods. Complex AI agents suddenly become... manageable. Anyone actually building with this approach, or are we all still throwing prompts at GPT and hoping for the best?