Agentic AI vs Generative AI: What's the Difference?
Generative AI creates content, while agentic AI takes action. Ask generative AI to write a refund email and it writes the email. Ask agentic AI to resolve a refund request and it can check the order, apply your policy, issue the refund and send the email.
The two aren't competitors. Agentic AI is usually built on top of generative AI, and adds the ability to plan and use tools.
At a glance
| Generative AI | Agentic AI | |
|---|---|---|
| Produces | Text, images, code, audio | Completed tasks |
| Works from | A prompt | A goal |
| Steps | Usually one response | Several, chosen as it goes |
| Uses tools | Not necessarily | Yes |
| Who acts on the output | A person | The system, within limits |
| Biggest risk | Wrong or made-up content | Wrong actions |
What generative AI does
Generative AI models learn patterns from large amounts of data and use them to produce new content. They're useful for drafting, summarizing, translating, brainstorming and writing code.
A person remains in control. You read the output, decide whether it's right, and do something with it.
What agentic AI does
Agentic AI wraps a model in a loop. It looks at the goal, decides on a next step, uses a tool, checks the result and repeats until it's finished or stuck.
That loop needs more than a model:
- Tools to search, read and change other systems
- Memory to keep track of what has happened
- Instructions that define its job and its limits
- Controls such as approvals and logs
The same job, done two ways
Suppose you're preparing for a sales meeting with a new prospect.
Using generative AI: you paste in the company's details and ask for a briefing. It gives you a useful summary. You collected the information, and you decide what to do next.
Using agentic AI: you ask it to prepare for tomorrow's meetings. It checks your calendar, looks up each company, pulls past emails from your CRM and leaves a briefing for each one in your notes.
Is ChatGPT agentic AI?
ChatGPT is mainly known as a generative AI product, because its core use is creating text in conversation. When a product like this gets tools and can work through multi-step tasks on its own, it's acting in an agentic way. Many AI products now do both, so the useful question is what a given feature actually does.
Why the difference matters to your business
The risk is different. Bad generative output is usually caught when someone reads it. An agent's mistake may already have reached a customer or changed a record by the time anyone sees it.
The cost is different. Generative AI can be adopted through a simple subscription. Agents need integrations, testing and oversight.
The value is different. Generative AI mostly helps a person work faster. Agents can take over whole tasks, although only some tasks are suited to that.
Which should you start with?
Start with generative AI if your team spends its time drafting, summarizing or searching for information. It's quick to try and low in risk.
Consider agentic AI when a process involves several systems and people spend their time moving information between them. Even then, begin with the agent drafting while a person approves. Read agentic automation to see when this approach is a good fit.
Talk to us
Wondering where each one fits in your business? DPR Software Labs is an AI-native software lab that can help you find the simplest useful starting point.