Deepanshi

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𝗛𝗢𝗪 𝗔𝗚𝗘𝗡𝗧𝗜𝗖 𝗔𝗜 𝗪𝗢𝗥𝗞𝗦 — 𝗙𝗥𝗢𝗠 𝗜𝗡𝗣𝗨𝗧 𝗧𝗢 𝗔𝗖𝗧𝗜𝗢𝗡 Agentic AI isn't just about generating an answer. It takes information, reasons over it, plans what to do, uses tools, learns from feedback, and takes action toward a goal. Here’s the workflow: 01 — 𝗜𝗡𝗣𝗨𝗧 𝗦𝗢𝗨𝗥𝗖𝗘𝗦 An agent can collect information from multiple sources: → Knowledge bases → User queries → APIs → Sensor data → System logs → Web data The more relevant context it has, the better it can understand the task. 02 — 𝗔𝗜 𝗣𝗥𝗢𝗖𝗘𝗦𝗦𝗜𝗡𝗚 This is where the intelligence happens. → Query Analysis → Reasoning → Memory Retrieval → Planning → Tool Selection → Context Management Instead of immediately responding, the system can determine what information it needs and what steps are required. 03 — 𝗔𝗖𝗧𝗜𝗢𝗡 𝗟𝗔𝗬𝗘𝗥 Once the plan is ready, the agent can execute. → Make decisions → Execute tasks → Collaborate with other agents → Handle errors → Learn from feedback → Schedule future actions This is where AI moves from “answering” to “doing.” 04 — 𝗙𝗘𝗘𝗗𝗕𝗔𝗖𝗞 𝗟𝗢𝗢𝗣 A strong agent doesn't simply perform an action and stop. It can observe the result, identify problems, use feedback, and adjust the next step. That creates a continuous loop: 𝗨𝗻𝗱𝗲𝗿𝘀𝘁𝗮𝗻𝗱 → 𝗥𝗲𝗮𝘀𝗼𝗻 → 𝗣𝗹𝗮𝗻 → 𝗔𝗰𝘁 → 𝗢𝗯𝘀𝗲𝗿𝘃𝗲 → 𝗜𝗺𝗽𝗿𝗼𝘃𝗲 05 — 𝗢𝗨𝗧𝗣𝗨𝗧 After processing the information and completing the required actions, the system generates the final response or result. 𝗧𝗛𝗘 𝗕𝗜𝗚 𝗣𝗜𝗖𝗧𝗨𝗥𝗘: Traditional AI: 𝗜𝗻𝗽𝘂𝘁 → 𝗠𝗼𝗱𝗲𝗹 → 𝗢𝘂𝘁𝗽𝘂𝘁 Agentic AI: 𝗜𝗻𝗽𝘂𝘁 → 𝗥𝗲𝗮𝘀𝗼𝗻 → 𝗥𝗲𝘁𝗿𝗶𝗲𝘃𝗲 → 𝗣𝗹𝗮𝗻 → 𝗖𝗵𝗼𝗼𝘀𝗲 𝗧𝗼𝗼𝗹𝘀 → 𝗔𝗰𝘁 → 𝗙𝗲𝗲𝗱𝗯𝗮𝗰𝗸 → 𝗜𝘁𝗲𝗿𝗮𝘁𝗲 That’s the real shift. 𝗔𝗜 𝗶𝘀 𝗺𝗼𝘃𝗶𝗻𝗴 𝗳𝗿𝗼𝗺 𝗴𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝗻𝗴 𝗿𝗲𝘀𝗽𝗼𝗻𝘀𝗲𝘀 𝘁𝗼 𝗲𝘅𝗲𝗰𝘂𝘁𝗶𝗻𝗴 𝗴𝗼𝗮𝗹𝘀. Save this diagram if you're learning about AI agents. Follow @Viveksahu1215 for more AI, tools, automation and tech insights. #AI #AgenticAI #AIAgents #ArtificialIntelligence #AIWorkflow #Automation #AITools

𝗛𝗢𝗪 𝗧𝗢 𝗕𝗨𝗜𝗟𝗗 𝗔𝗡 𝗔𝗜 𝗔𝗚𝗘𝗡𝗧 — 𝟴 𝗞𝗘𝗬 𝗦𝗧𝗘𝗣𝗦 Building an AI agent is more than connecting an LLM to a chatbot. A reliable agent needs the right purpose, tools, memory, orchestration, interface, and testing. Here’s the complete workflow: 01 — 𝗗𝗘𝗙𝗜𝗡𝗘 𝗣𝗨𝗥𝗣𝗢𝗦𝗘 & 𝗦𝗖𝗢𝗣𝗘 Start with the problem. → Define the use case → Understand user needs → Set success criteria → Identify constraints If you don't know what the agent should accomplish, adding more tools won't fix the problem. 02 — 𝗗𝗘𝗦𝗜𝗚𝗡 𝗧𝗛𝗘 𝗦𝗬𝗦𝗧𝗘𝗠 𝗣𝗥𝗢𝗠𝗣𝗧 Give the agent clear instructions. → Goals → Role/persona → Instructions → Guardrails The system prompt defines how the agent should behave. 03 — 𝗖𝗛𝗢𝗢𝗦𝗘 𝗧𝗛𝗘 𝗥𝗜𝗚𝗛𝗧 𝗟𝗟𝗠 Consider more than just model quality. → Base model → Context window → Parameters → Cost → Latency The best model is the one that fits your specific workload. 04 — 𝗔𝗗𝗗 𝗧𝗢𝗢𝗟𝗦 & 𝗜𝗡𝗧𝗘𝗚𝗥𝗔𝗧𝗜𝗢𝗡𝗦 This is where the agent gets real capabilities. → APIs → Databases → MCP servers → Custom functions → External services → Other AI agents Tools allow the agent to interact with systems beyond the model. 05 — 𝗕𝗨𝗜𝗟𝗗 𝗠𝗘𝗠𝗢𝗥𝗬 Agents often need context beyond a single conversation. Possible memory systems include: → Conversation history → Working memory → Vector databases → SQL databases → File storage Good memory helps the agent maintain relevant context across tasks. 06 — 𝗗𝗘𝗦𝗜𝗚𝗡 𝗢𝗥𝗖𝗛𝗘𝗦𝗧𝗥𝗔𝗧𝗜𝗢𝗡 Now define how everything works together. → Workflows → Triggers → Parameters → Message queues → Agent-to-agent communication → Error handling This is what turns individual components into a working system. 07 — 𝗕𝗨𝗜𝗟𝗗 𝗧𝗛𝗘 𝗨𝗦𝗘𝗥 𝗜𝗡𝗧𝗘𝗥𝗙𝗔𝗖𝗘 Give users a practical way to interact with the agent. → Chat interface → Web app → API endpoint → Slack/Discord bot 08 — 𝗧𝗘𝗦𝗧 & 𝗘𝗩𝗔𝗟𝗨𝗔𝗧𝗘 Never assume the agent works just because the demo works. Test: → Accuracy → Quality → Latency → Tool usage → Failure cases → Real-world workflows Then iterate and improve. 𝗧𝗛𝗘 𝗕𝗜𝗚 𝗣𝗜𝗖𝗧𝗨𝗥𝗘: 𝗣𝘂𝗿𝗽𝗼𝘀𝗲 → 𝗣𝗿𝗼𝗺𝗽𝘁 → 𝗟𝗟𝗠 → 𝗧𝗼𝗼𝗹𝘀 → 𝗠𝗲𝗺𝗼𝗿𝘆 → 𝗢𝗿𝗰𝗵𝗲𝘀𝘁𝗿𝗮𝘁𝗶𝗼𝗻 → 𝗜𝗻𝘁𝗲𝗿𝗳𝗮𝗰𝗲 → 𝗘𝘃𝗮𝗹𝘂𝗮𝘁𝗶𝗼𝗻 𝗧𝗛𝗘 𝗞𝗘𝗬 𝗟𝗘𝗦𝗦𝗢𝗡: A powerful AI agent isn't just a powerful model. It's a well-designed system where the model, tools, memory, workflows, and guardrails work together. Save this framework if you're planning to build an AI agent. Follow @Viveksahu1215 for more AI, tools, automation and tech insights. #AI #AIAgents #AgenticAI #ArtificialIntelligence #AIEngineering #Automation #AITools #Tech