AI Agent Architecture: Building Systems That Think, Plan, and Act

System Design Deep Dive — #4 of 20 | This is part of a 20-post series covering the most critical system design topics. Follow to get the next one. Cognition's Devin made headlines as the first AI software engineer, raising $175M at a $2B valuation before even launching publicly. GitHub Copilot's agent mode handles complex multi-file refactors. Cursor's Composer rewrites code across entire projects. These aren't chatbots -- they're AI agents that reason about multi-step tasks, use external tools, maintain memory, and take real-world actions. The architectural patterns behind them are the hottest topic in AI engineering right now. TL;DR: AI agents operate in observe-think-act loops, not single-shot prompt-response cycles. The core architecture has four components: a planning module (task decomposition), a memory system (short-term + long-term), tool integration (APIs, code execution, search), and a reasoning engine (typically ReAct: Reason + Act). Always build with guardrails, budget cap