Table of Contents
Introduction
Science is ever-evolving, and similarly, Artificial Intelligence (AI) is continuously evolving, leading to different branches of computer science that result in different functions. The recent applications of Generative AI stand to transform many significant workflows in our lives. While Generative AI has been significant in creating unique content in the form of text, images, or videos, Agentic AI, which is another revolutionary branch of AI, is leap years ahead in performing tasks. They are intelligent computer systems that can autonomously perform tasks by independently making decisions and using logical reasoning.
Recently, AI workflows have witnessed increasing adoption in various industries and organizational processes. However, a clear understanding of the functions, applications, and benefits is essential.
This blog will explore the difference between Agentic AI and Generative AI. It will help organizations and small and medium enterprises plan AI adoption efficiently that will suit their business requirements.
What is Agentic AI?
Agentic AI is a type of AI that is capable of independently performing tasks, making critical decisions, reasoning, and learning without human intervention. They are AI systems that have achieved a level of autonomy to plan their course of action in order to perform a task.
Agentic AI is like a personal assistant that can think, decide, plan, and reason to perform tasks. Gartner forecasts that by 2028, agentic AI will autonomously make at least 15% of day-to-day work decisions. They are highly adaptable to new situations as they learn from their environments. They efficiently handle complex tasks with effective problem-solving techniques.
What is Generative AI?
Generative AI, on the other hand, is a special branch of AI that is efficient at creating new content in various formats, like images, text, videos, music, or even codes. Fundamentally, generative AI learns from existing data and uses this information to create unique content.
Generative AI tools rely on machine learning and neural network models through vast data sets to analyze and create new content. But it comes with its shortcomings, and usually, the output is as good as the input data that the system uses to learn. If there are discrepancies in the learning data set, it will result in the output. Overall, a generative AI system relies on predicting the best outcome based on the pattern it analyzes from the data.
Whats the difference between Agentic AI and Generative AI?
Agentic AI and Generative AI may sound similar since both are evolved branches of artificial intelligence, but both are quite distinct in their functionality, task orientation, output generation, etc.
| Features | Agentic AI | Generative AI |
|---|---|---|
| Autonomy | Has a high level of autonomous thinking to perform critical tasks | Limited ability for autonomous thinking. Require human inputs. |
| Focus | It is driven by goal and can plan the best course of action to reach its goal. | It focuses on specific tasks and follows pre-defined instructions |
| Function | Its primary function is to perceive its environment, analyze information, plan a course of action, to achieve a specific goal | Its major function is to learn patterns from data sets and use them to create new content in the form of images, text, videos, or music. |
| Learning | Continually learns to improve its knowledge of performing its goal. | Limited scope of learning within pre-desinged set of rules. |
| Decision-making | It uses logical reasoning, analysis, & critical thinking to make decisions | It cannot make decisions on its own and needs human inputs. |
| Complexity | It is capable of handling dynamic situations | It can only handle simple tasks. |
| Adoption to surrounding | It can quickly adapt to its surroundings. | It has limited flexibility to adopt to changing environments. |
| Reaction to change | Autonomously takes control in planning its course of action for a new goal | It is limited in its ability to react to a change |
Real-world applications of Agentic AI and Generative AI
AI advancement is going through bullet train speed, and its applications are revolutionizing several industries and workflows. Both Generative AI and Agentic AI are witnessing increasing applications in diverse industries.
Agentic AI
🔹Autonomous cars: The application of Agentic AI to drive a car has been one of the revolutionary applications of AI. Agentic AI systems in a vehicle can perceive its environment and make critical decisions on driving speed, traffic rules, etc. It learns from every driving experience to improve its navigation and control of the vehicle for a safe ride in its further journeys.
🔹Inventory management: Agentic AI is perfectly apt to manage inventories of any scale. They can predict demand and take real-time action to improve supply movements, monitor inventory stock levels, and create alerts for stock refilling based on priority.
🔹Healthcare: In healthcare, they can assist with diagnosis, treatment plan, prognosis of disease, etc. It can quickly scan vast medical data to analyze patterns and suggest healthcare providers with the best course of action for patient care.
Generative AI
• Personal assistant: As a personal assistant, Generative AI helps users automate repetitive tasks that don’t involve much reasoning and thinking. Most of us must be using Google Assistant, Amazon Alexa, or Apple Siri to search for entertainment content, play music, update on weather, or set up reminders, etc. Personal assistants improve convenience, efficiency, and support in performing regular tasks.
• Customer support: In customer support, AI chatbots are helping support executives answer queries, resolve FAQs, assist in troubleshooting, and guide users through various processes with high customer satisfaction. It allows more time for human agents to focus on critical tasks.
• E-commerce: In e-commerce businesses, generative AI can efficiently create unique product descriptions and related images. They are also helpful in creating a personalized shopping experience that improves user experience. They can also create virtual try-on options for users to try clothes and cosmetics before buying.
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Conclusion
As we wrap up our blog into Agentic AI and Generative AI, we can see that both pack a punch in the AI world, but they have different jobs. Generative AI shines when it comes to making new imaginative stuff, pushing what’s possible in art, design, and even scientific breakthroughs. On the other side, Agentic AI is all about being independent and making choices, letting systems handle tricky situations and reach specific goals without much human intervention.
The main point isn’t to see these as rivals but as teammates. The future of AI lies in how well they work together, where the creative skills of generative systems boost how agentic systems make decisions. Agentic frameworks give the needed control and direction to generative AI models.
As we go ahead, we need to keep in mind the ethical consequences of both technologies. To ensure these powerful tools help society, we’ll need to develop and use them wisely. These tools can do amazing things, from making stunning art to handling important jobs. Both Agentic and Generative AI have the power to change our world in big ways. To get the most out of their potential for new ideas and progress, we first need to understand how they’re different and what each one does best.
Frequently Asked Questions
Yes! These two have a strong chance to work well together. You can think about it as an Agentic AI might use Generative AI to create reports or handle communication tasks.
Neither one has an edge in terms of being “more advanced.” They tackle different challenges and have their strong points. How “advanced” they are depends on what you’re using them for.
Even though this smart tech creates new things, it gets its outputs from the data it’s studied before. So yeah, what it makes is rooted in the patterns it learns from its data analysis.
Nisha Sneha
Nisha Sneha is a passionate content writer with 5 years of experience creating impactful content for SAAS products, new-age technologies, and software applications. Currently, she is contributing to Kenyt.AI by crafting engaging content for its readers. Creating captivating content that provides accurate information about the latest advancements in science and technology has been at the core of her creativity.
In addition to writing, she enjoys gardening, reading, and swimming as hobbies.
