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Rahul Agarwal
Founder | Agentic AI... • 6h
Stop calling everything an AI agent. Most of it isn’t even close. The label sounds impressive, but it creates confusion across teams and slows decisions down. Leaders hear “agent” and expect autonomy, reasoning, and independent execution in systems. That assumption changes risk perception. And everything suddenly feels harder. Let’s separate what’s actually being built today across most AI-driven workflows. 𝗖𝗵𝗮𝘁𝗯𝗼𝘁𝘀 Simple input and response loops. No actions. No decisions. Just conversation interfaces. They help with answers. But don’t do the work. 𝗥𝗣𝗔 𝘀𝘆𝘀𝘁𝗲𝗺𝘀 Rule-based automation running predefined steps across repetitive business processes and back-office tasks. They follow instructions strictly. No flexibility under pressure. One unexpected input breaks them. They execute, not think. 𝗥𝗔𝗚 𝘀𝗲𝘁𝘂𝗽𝘀 Pull relevant data, attach context, and generate responses grounded in company-specific information. Great for internal knowledge. Reliable for structured queries. But there’s no planning. No real execution layer. Now comes the difference that actually matters when designing intelligent systems at scale. 𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗔𝗜 This is where systems begin planning, deciding, and acting across tools with clear objectives. They break tasks down. Call external systems. Update workflows dynamically. They retain context over time. And adapt based on feedback. This is closer to a digital operator than a reactive assistant responding to prompts. Different approaches solve different problems. They require different controls. When everything becomes an “agent,” clarity disappears and adoption slows across organizations. Sometimes simple systems win. And that’s exactly what you need. So ask yourself clearly. What is your objective and what will you need to fulfil that objective?
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