Agentic AI vs Generative AI: One Creates, the Other Acts — Here’s the Difference

Even most people are still not aware that generative AI and agentic AI are not the same. They’re not. One answers to your call. The other one has its own objectives. This breakdown will help you understand what is agentic AI, what is generative AI, some examples of both, and which one will be most impactful to the world of work in the future.

When ChatGPT launched in November 2022, it crossed 100 million users in just two months faster than any consumer application in history, according to a UBS analyst report that moment made generative AI a household name, but just as people started getting comfortable with asking AI to write emails or generate images, a more powerful concept started making noise in tech circles, and that is “agentic AI”.

The two types of AI are not identical and the difference is very important. One waits until the other says “please”. When it comes to understanding the direction of AI, and where it’s going, understanding the difference between agentic AI vs generative AI is no longer a luxury, it’s a necessity.

What Is Generative AI – And What Is It Doing?

Generative AI term generally describes AI systems that are trained to generate original content text, images, video, audio or code in response to a prompt. Those models are trained on massive datasets and detect patterns in billions of examples and apply that knowledge to produce new and human-like outputs on-demand.

According to McKinsey’s 2023 State of AI report, generative AI has the potential to add $2.6 to $4.4 trillion annually across global industries primarily through content creation, code generation and customer service automation. GitHub’s own research found that developers using Copilot or a generative AI coding tool completed tasks 55% faster than those working without it.

What Generative AI Does Well

Generative AI works well in scenarios where there is an input-output relationship that involves human validation. It composes first drafts of articles for blogs, advertisements and product descriptions. It summarizes extensive information into key points. It creates images, designs for products and scripts for videos. It enables personalized content generation engines using user feedback and it develops working code for developers to build upon. Essentially, it is a highly creative and a very powerful tool which remains responsive in its application.

What Is Agentic artificial intelligence (AI) – And Why It’s Different Entirely

Agentic AI capable of planning, decision making and taking action independently to accomplish a set objective with minimal human supervision during the process.

The AI is not only responsive to your instructions, but breaks down your task into executable actions based on the goal you’ve set out for. It then acts through the integration of devices or machines, evaluates itself and adjusts to act accordingly. 

“The transition between AI answering questions to AI accomplishing missions is called agentic AI”.

If you told the generative AI to “conduct research on our top three competitors and generate a summary report,” you’d get your nicely written summary report. If you provide the exact same command to an agentic AI platform, the machine will conduct research independently on the Internet, draw the necessary conclusions, create the report and even finish it without you having to perform any further actions.

As per Gartner report for 2024, agentic AI has been put among the top 10 strategic technology trends, with a prediction that at least 15% of routine business decisions will be taken autonomously by agentic AI systems by the year 2028. These figures show that the world is making a radical turn in its future way of doing business operations.

What Agentic AI Does Differently

Generative AI reacts, agentic AI acts. Looks at a situation and selects the best way to proceed without being told what to do. It breaks down large problems into smaller ones that can be addressed sequentially. It integrates to external API services, databases, browsers and enterprise applications to accomplish the work. Unlike agentic ai that works on data from the previous conversation, it does not create text based on a job but stores the information acquired from it. 

Agentic AI vs Generative AI Real World Examples

This report explores the potential of generative AI and its role in the real world. Already, generative AI is transforming the cybersecurity industry by empowering security teams to detect threats faster, automate incident reports and analyse huge amounts of security data in real time. The systems could potentially detect cyber threats and take preemptive measures to prevent them without requiring constant human supervision, thanks to developments in agentic AI. 

From marketing professionals to software developers, everyone is making use of AI tools such as ChatGPT to create advertising materials, blog posts and social media content in a matter of minutes. GitHub Copilot allows software developers to write coding in a more efficient manner by automatically completing code functions, creating test scenarios and even explaining legacy code. Artists and designers are using AI-based tools like Midjourney and DALL-E for producing images based on text descriptions, which include logos, branding designs and product mockups, etc.

According to a report by Zendesk, the adoption of these tools in companies resulted in a 30 percent drop in ticket volumes.

Agentic AI in the Real World

While the use cases for agentic AI are still unfolding in reality, those that have been deployed so far are impressive. Without the need for a human to look at each data point, Propeller Health is incorporating agentic AI into its smart technology that is constantly monitoring patient medication use, air quality in the environment and can automatically notify the healthcare provider when action is required. 

Amazon’s fulfillment centres employ autonomous robotic agents that facilitate the quick decision-making for picking, routing and managing inventory in real-time during millions of operations. Fintech companies are using agentic AI to track market changes in real time, and making instant decisions to rebalance allocations of their portfolios in response to economic indicators. 

Use Cases of Generative AI and Agentic AI — Where Each One Wins

Applications of Generative AI

Generative AI is the appropriate tool when the work is creative, output-driven and where the human in the loop at the review stage gives it an advantage. It pulls the content creation engines of digital marketing firms in need of hundreds of articles per month with SEO-optimization. It drives the creation of product descriptions of big e-commerce catalogs where it is just not possible to write thousands of descriptions by hand. It facilitates speedy prototyping of codes hence engineering teams can develop an idea for working drafts more rapidly. And it supports personalization engines in retail and media that will show different content to each user depending on behavioral data.

A space where McKinsey’s research shows personalization can deliver revenue increases of 10 to 15 percent.

Applications of Agentic AI

Actual case studies for agentic AI are not many, but those already developed are remarkable. For instance, Propeller Health uses an agentic AI in its smart technology in which the system monitors the consumption of drugs by patients, measures the level of pollution in the air and alerts healthcare professionals when necessary actions have to be taken and this is done without any human analyzing the situation.

On the other hand, Amazon’s automated distribution centers utilize robotic agents who make live route, picking and inventory decisions on millions of orders each day. Finally, fintechs use agentic AI algorithms that analyze live market data and rebalance investments in their portfolios accordingly. Previously, humans had to stare at their computers all day long to make similar decisions.

Gen AI vs Agentic AI: They’re Not Competing — They’re Collaborating

One of the biggest myths about the distinction between agentic AI and generative AI concerns rivalry between them. There is no such thing. Agentic AI works using generative AI as its core component. It is a bit like a partnership between a project manager and a specialist.

The agentic system identifies the sequence of actions that need to be undertaken and their order. The generative AI model takes care of the heavy lifting part be it creation or analytics. Then the AI agent takes the output of the operation and uses it to prompt the next action be it an email sending, a database update, a report or a decision-making action.

To understand it, let’s take an example of agentic AI for sales purposes. Agentic AI would use GPT-4 to write an email outreach letter, followed by the CRM tool to send the email, analyze its response and organize a phone call with the recipient.

It is not about generative AI and agentic AI. It is knowing that agentic AI is the next step that has been constructed on the top of generative AI.

Agentic AI and Generative AI: In-depth Comparative Analysis

The key differences are summarized in the table below. This is a good reference to keep, the more you’ll see these tools in action the more sense you’ll find in the differences. 

The most critical thing to learn from the above table is that generative AI is responsive and agentic AI is pro-active. Generative AI does nothing until someone asks for it, whereas agentic AI tries to achieve a goal.

Which One Should You Be Paying Attention to Right Now?

Gen AI is a fully matured technology that is already integrated into the software you use every day, such as Google Docs, Microsoft 365, Notion, Figma and many other enterprise software platforms. The gap is not whether or not these technologies are available but how well you know and how to apply them to improve your workflow is the real gap.

The future of AI is agentic. Much remains experimental at the moment, the technology stack and the necessary ecosystem are rapidly developing. According to the 2024 AI hype cycle by Gartner, agentic AI technology is already on the Peak of Inflated Expectations stage and should move towards the Plateau of Productivity in two to five years.

The tools for developing agentic AI are also in place, such as AutoGPT, CrewAI, LangChain, and Microsoft Copilot Studio. These are the platforms where enterprise organizations can deploy agentic AI agents. Investing in Pursuing the Agentic AI Technology will be the ones who will be at the forefront of the Agentic Operations in the next 3-5 years. 

Conclusion: Two Sides Of AI Revolution

Generative AI and agentic AI are not rival technologies; it is a step in the chronological development of AI. Gen AI provided us with an excellent creative assistant. By making that assistant an agentic AI, they transform it into a competent and self-determined operator able to pursue objectives, rather than answer questions.

Assuming that the brain is the generative AI, the brain with hands, initiative, and a to-do list is the agentic AI. The transition between the stimulation of AI to deployment of AI agents is already being implemented. Now that is an informed advantage that will be able to distinguish the people who will be developing technology versus those who will be responding to it.