Agentic AI Patterns for Production — ReAct, Code-Act & Tool-Use Loop Explained

Most AI agents fail in production. Not because the model is bad, but because the architecture is wrong. I've been building enterprise AI systems for cloud migrations, data platform modernization, and large-scale automation. And the #1 mistake I see teams make? They jump straight into coding without choosing the right agentic architecture pattern. So I'm launching a 5-part video series: Agentic AI Architectures — A Complete Guide. Part 1 just dropped: The Foundation — Single-Agent Architectures. Here's what it covers: ReAct (Reason + Act) — The agent explicitly writes its reasoning before every action. Transparent. Debuggable. The gold standard for complex reasoning tasks. But watch out for infinite loops. Code-Act — Instead of calling predefined tools, the agent writes and executes actual code. This is what powers Claude Code and OpenAI's Code Interpreter. Code is a universal action space — one script can replace ten tool calls. Tool-Use Loop — The minimal pattern. No chain-of-thought, no verbose reasoning. Just task in, tool calls, result out. Cheap, fast, and what most API integrations look like today. But it's a black box when things go wrong. The key insight from this episode? These patterns aren't mutually exclusive. A ReAct agent can invoke Code-Act for a specific step. A Tool-Use Loop can escalate to ReAct when it hits complexity. The best architectures are composable. Coming up in the series: Part 2 → Multi-Agent (Orchestrator-Worker, Swarms, Debate) Part 3 → Workflow-Based (DAGs, State Machines, Plan-and-Execute) Part 4 → Reflection, Human-in-the-Loop, Agentic RAG Part 5 → Frameworks, Production Concerns & Decision Guide If you're building AI agents for enterprise — cloud migrations, data pipelines, automation — this series is for you. Read the full breakdown (with architecture diagrams and best use cases): https://mactores.com/blog/how-to-choo... Drop a comment: which single-agent pattern are you using today? #agenticai #aiarchitecture #cloudmigration #dataengineering #genai #aws #llms #aiagents #machinelearning #enterpriseai