Daniel

𝗧𝗢𝗣 𝟰 𝗔𝗚𝗘𝗡𝗧𝗜𝗖 𝗔𝗜 𝗗𝗘𝗦𝗜𝗚𝗡 𝗣𝗔𝗧𝗧𝗘𝗥𝗡𝗦 𝗬𝗢𝗨 𝗦𝗛𝗢𝗨𝗟𝗗 𝗞𝗡𝗢𝗪 Building an AI agent isn't just about connecting an LLM to a tool. The real power comes from choosing the right architecture for the problem. Here are 4 important agentic AI patterns: 01 — 𝗦𝗘𝗟𝗙-𝗥𝗘𝗙𝗟𝗘𝗖𝗧𝗜𝗢𝗡 𝗣𝗔𝗧𝗧𝗘𝗥𝗡 The AI generates an answer, evaluates its own response, identifies weaknesses, and improves it. 𝗙𝗹𝗼𝘄: Generate → Evaluate → Reflect → Improve → Final Output Useful for: → Writing → Code generation → Reasoning tasks → Quality improvement 02 — 𝗥𝗘𝗔𝗖𝗧 𝗣𝗔𝗧𝗧𝗘𝗥𝗡 ReAct combines reasoning with action. The agent: → Understands the task → Decides what to do → Uses a tool or API → Observes the result → Reasons again → Continues until the task is complete This is useful when an agent needs real-time information or external tools. 03 — 𝗠𝗨𝗟𝗧𝗜-𝗔𝗚𝗘𝗡𝗧 𝗣𝗔𝗧𝗧𝗘𝗥𝗡 Instead of asking one AI to handle everything, multiple specialized agents collaborate. For example: → Research Agent → gathers information → Developer Agent → writes code → Content Agent → creates content → Project Agent → coordinates everything Each agent has a specific responsibility. 04 — 𝗣𝗟𝗔𝗡𝗡𝗜𝗡𝗚 𝗣𝗔𝗧𝗧𝗘𝗥𝗡 A complex goal is broken into smaller tasks. 𝗚𝗼𝗮𝗹 → 𝗣𝗹𝗮𝗻 → 𝗘𝘅𝗲𝗰𝘂𝘁𝗲 → 𝗘𝘃𝗮𝗹𝘂𝗮𝘁𝗲 → 𝗥𝗲𝗽𝗹𝗮𝗻 If something fails, the agent can adjust the plan and try another approach. 𝗪𝗛𝗜𝗖𝗛 𝗢𝗡𝗘 𝗦𝗛𝗢𝗨𝗟𝗗 𝗬𝗢𝗨 𝗨𝗦𝗘? 𝗦𝗲𝗹𝗳-𝗥𝗲𝗳𝗹𝗲𝗰𝘁𝗶𝗼𝗻 → When quality and iteration matter 𝗥𝗲𝗔𝗖𝗧 → When the agent needs tools and real-world feedback 𝗠𝘂𝗹𝘁𝗶-𝗔𝗴𝗲𝗻𝘁 → When the problem needs specialized roles 𝗣𝗹𝗮𝗻𝗻𝗶𝗻𝗴 → When the task involves multiple dependent steps 𝗧𝗛𝗘 𝗕𝗜𝗚 𝗜𝗗𝗘𝗔: There is no single “best” agent architecture. The right pattern depends on the task, tools, complexity, reliability requirements, and level of autonomy you need. 𝗖𝗵𝗼𝗼𝘀𝗲 𝘁𝗵𝗲 𝗽𝗮𝘁𝘁𝗲𝗿𝗻 𝗯𝗮𝘀𝗲𝗱 𝗼𝗻 𝘁𝗵𝗲 𝗽𝗿𝗼𝗯𝗹𝗲𝗺 — 𝗻𝗼𝘁 𝘁𝗵𝗲 𝗵𝘆𝗽𝗲. Save this for when you’re designing your next AI agent. Follow @mayaislam_ai for more AI, tools, automation and tech insights. #AI #AgenticAI #AIAgents #ArtificialIntelligence #AIEngineering #Automation #AITools #Tech