What's the difference between agentic AI and regular chatbot AI?

Started by Olivia78, Jun 21, 2026, 11:39 PM

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Topic: What's the difference between agentic AI and regular chatbot AI?   Views(Read 125 times)

Olivia78

Everyone keeps saying AI is becoming agentic. What does that even mean? How is ChatGPT different from an agentic AI if they're both language models?

FairDos72

Chatbots answer questions. You ask something they respond. The interaction ends. Agentic AI takes goals and completes tasks across multiple steps. You ask for something to be done the AI plans executes and adapts based on outcomes

EventHorizon Crossing

ChatGPT is reactive. You prompt it responds. Agentic systems are proactive. They identify subtasks needed to reach the goal then handle them autonomously. Claude writing code to solve a problem then testing it then debugging based on test results is agentic
Just here collapsing wave functions :)

BankHolidayBlues87

The technical difference is autonomy and planning. Agentic systems maintain state across interactions reason about what comes next and call external tools. They don't just generate text they actually do things

Jackson79

Google Gemini 3.5 is marketed as agentic. It can handle research coding and reasoning tasks across multiple steps. You ask for research on a topic it breaks into subtasks searches information synthesizes and writes report without asking for permission on each step
Have you tried turning it off and on again?

Candle28

OpenAI's o1 model introduced reasoning capabilities that enable planning. Internal chain-of-thought reasoning let models think through complex problems. That's foundational to agentic behavior

Woven Sasha

Real-world example: agentic AI helps drug discovery. You describe a compound problem. The AI designs experiments selects materials configures lab equipment runs tests and adjusts based on results. That's multiple steps with feedback loops

Joel5

Another example: code generation. Chatbot writes code snippet. Agentic system writes code tests it debugging based on failures then iterates until it works. That's agency not just text generation
Always open to a good discussion

Gold Terry

The capability requirement for agentic is higher reasoning. The model needs to understand task decomposition understand tool limitations and adapt when things don't work. Not every model can do this well

Joel96

Business impact is huge. Agentic AI does work not just answers questions. Customer support becomes handled autonomously. Research happens without human supervision. Coding accelerates beyond just suggestions
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Solid Gary

The frontier labs are all racing toward agentic capabilities because it's where business value concentrates. Pure reasoning will plateau but autonomous task completion scales indefinitely

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