6-Agentic AI Design Patterns

Teams waste months reinventing AI Agent architectures that already exist Here are the 6 patterns the best products are already using... Everyone's building AI Agents now. But the ones who actually ship robust, production-ready systems? They don't just prompt; they understand the core architectures beneath them. The same way you can't build a scalable app without knowing system design, you can't build reliable AI Agents without understanding core patterns that power them. 📌 Let me break down all 6 so you can apply them: 1\ ReAct Agent (used by most agents) - Alternates between reasoning with an LLM and acting via tools like Google or email - The backbone of almost every AI agent you've used Code Sample to try: https://lnkd.in/gq6xi7-7 2\ CodeAct Agent (used by Manus) - Interacts with a coding sandbox to think, plan, and produce code - Best for: complex code generation and autonomous dev workflows Code Sample to try:https://lnkd.in/gWFXtetA 3\ Agentic RAG (used by Perplexity, Copilot and others) - A Meta Agent retrieves data, a Researcher searches, and an Evaluator scores quality - Loops until the response passes, includes human verification when needed Code Sample to try: https://lnkd.in/gEJqgQTJ 4\ CUA — Computer-Using Agent (used by OpenAI Operator) - Agents use tools like cursor to perform computer actions on your behalf - Combines VLM + LLM + Browser Sandbox + Memory + Knowledge Code Sample to try: https://lnkd.in/gCdxpUBi 5\ Self-Reflection (used by most agents) - LLM generates a draft → Critique LLM reviews it → Generator refines it - Best for: content, code, or analysis requiring high accuracy Code Sample to try: https://lnkd.in/g3P4Xu3Z 6\ Multi-Agent Interoperability (used by most agents) - Agents built on different frameworks communicate via A2A Protocol - Best for: enterprise workflows with specialised, cross-platform agents Code Sample to try: https://lnkd.in/grFSPM5u If you want to understand AI agent concepts deeper, my free n