Agentic AI vs. generative AI

Both agentic AI and generative AI (gen AI) offer productivity benefits by assisting, augmenting, and optimizing tasks and processes. Both are forms of artificial intelligence that use large language models (LLMs). 

When comparing the 2, think of agentic AI as proactive and gen AI as reactive. 

• Agentic AI is a system that can proactively set and complete goals with minimal human oversight. If part of accomplishing that goal involves creating content, gen AI tools handle that task. Agentic AI becomes an agent of the user and/or system.
• Gen AI is a tool that creates new content in reaction to a prompt. It can be a component of an agentic system, but it doesn’t act on its own to complete a task. It has no agency.

Agentic AI and gen AI do work collaboratively. Agentic AI systems may use gen AI to converse with a user, independently create content as part of a greater goal, or communicate with external tools. In other words, gen AI is a critical part of agentic AI’s “cognitive” process. 

Explore agentic AI use cases

The line between agentic AI and gen AI can feel blurry because they both begin with a prompt from a user and typically exist in a chatbot-like format. Plus, many applications that were once purely gen AI now include agentic elements—a trend that’ll likely continue. 

For example, many of the popular chatbot platforms (ChatGPT, Gemini, Claude, etc.) automatically initiate a web search, parse the data, and return it as part of the conversation. This is a primitive form of agentic AI.  

Agentic AI and gen AI differ in their ability to act independently and collaborate with external tools

To better explain the difference between the 2 technologies, let’s explore a hypothetical use case: 

A sales representative wants to use AI to write a follow-up email to a sales lead. 

With generative AI, the sales representative would open a gen AI interface and type a prompt like, “Write a polite and professional follow-up email to Maria Wang about our proposal.” The gen AI instantly produces a draft of the email and has fulfilled its purpose. It’s now up to the sales representative to copy that text, paste it into an email, enter the recipient’s email address, and hit send.

Now let’s explore how agentic AI would handle a similar task. 

Within an agentic system, the sales representative would set a rule or command in their customer relationship management (CRM) system. It might say something like, “For any sales lead I mark as ‘Follow-up required,’ wait 2 business days, then send a follow-up email.”

Once the sales representative marks Maria Wang as “Follow-up required,” the agentic workflow is triggered. The system has its instructions (the initial prompt) and independently lays out a plan to enact with the help of external tools. The plan may look something like this: 

1) After 2 business days, the system sends a request to the agentic workflow.

2) The system retrieves Maria’s details from the CRM.

3) Another tool fetches additional information about Maria (customer history, personalization details, company information, etc.) that provides context for the prompt for the follow-up email.

4) The system creates a prompt for the follow-up email and provides it to an integrated gen AI model, which writes the email text.

5) The system provides a draft of the follow-up email to the sales representative, who approves the email or sends it back to redraft.

6) If the sales representative approves the email, the system makes an application programming interface (API) call to Maria’s email service.

7) The system sends the email to Maria. 

8) The system updates the CRM to show the email has been sent. 

Agentic AI and gen AI differ in their ability to adapt 

Both types of AI are adaptable in their own way. Generative AI expresses its adaptability by producing content in many styles and for different contexts. Agentic AI showcases its adaptability by adjusting its plan and strategy in response to changing environmental conditions or new information.  

Agentic AI provides a framework for workloads that used to be classified as robotic process automation (RPA). The injection of AI makes agents far more adaptable to changes in their runtime environment. For example, screen-scraping bots would struggle with even minor changes on the target site, whereas agentic AI can adapt to changes and adjust its approach to harvesting data. Therefore, AI-powered agents can operate on a level that previously would've required human input.

Context understanding

Agentic AI uses context for action. Agentic systems have access to:

• The initial prompt.
• The state or conditions of the digital or physical environment.
• Available tools (API access, a gen AI application, etc.).
• Memory and past actions.

With all this information, plus mathematical formulas that help it navigate the data, an agentic system gains an understanding of context. This is what allows it to “reason” and take action. 

Meanwhile, generative AI uses context for creation. Because the goal of gen AI is to produce new content, generative applications have access to:

• The prompt.
• Conversational history.
• Data they were trained on.

With this information, plus machine learning techniques and deep learning algorithms, gen AI can draw information, create connections, and produce an output.