Artificial Intelligence, commonly understood as AI, has been widely discussed for decades. However, in recent days, terminologies such as generative AI and agentic AI have come into being. Although traditional AI provides users with a new way to identify new patterns and analyze data, gen AI helps craft new patterns and content such as text, images, videos, audio, and software code. In this blog, we will discuss agentic AI vs generative AI.
The Key Differences Between Agentic AI and Generative AI
Generative AI is artificial Intelligence capable of creating original content, including text, images, videos, audio, or software code based on the user’s prompt. Generative AI uses machine language models, commonly known as deep learning models. These are the algorithms that enhance the learning and decision-making processes of the human brain.
These models generally operate by recognizing and encoding the patterns and relationships in large number of data and then utilizing that information to comprehend user’s natural language questions. Finally, they generate high-quality text, images, and other content according to the information they were provided.
On the other hand, agentic AI is known as an AI system that is developed to make decisions freely and act with the ability to take complex goals, requiring less supervision. It combines the flexible features of large learning models with the precision of old programming. Agentic AI works anonymously to achieve a goal by using technologies such as natural language processing (NLPs), machine learning, reinforcement learning as well as knowledge representation.
This is a practical AI-powered approach, while generative AI is reactive to the user’s idea. Agentic AI is flexible and can used for various and changing situations and possesses agency that makes choices according to the context.
Features of Agentic AI and Generative AI
Both Agentic AI and Generative AI have various objectives and unique attributes that make them different from each other.
Key Features of Generative AI
Content creation: Generative AI is an expert in content generation. It is highly known for creating clear context, such as essays and solutions to complete problems. AI applications, including ChatGPT can generate answers, write list, as well as give advice based on user’s prompt. With the help of generative AI you can create code to streamline software development, making it convenient for developers of different levels of skills to write code. It is also help in custom CRM development.
Data Analysis: Generative AI is able to scan a wide range of data and work on that analysis to explore patterns and trends. The generative AI tools can simplify complex workflows, especially when creating a better customer experience.
Adaptability: Generative AI can adjust its output according to the prompt it received from the user. If the user is providing a specific prompt to the model, the output moves to match more to what the user is looking for.
Personalization: Generative AI models also help make customized references as well as experience based on the user’s prompts. This approach is also helping retail industries to understand every detail of their customer needs.
Key Features of Agentic AI
Decision-making: Agentic AI models have pre-defined plans and objectives through which they can access situations and figure out the path forward with the least human input.
Problem-solving: Agentic AI implements a four-step method for solving problems, including perceive, reasons, learn, and act. After that, LLM acts as a manager that scans perceived data to understand the situation. And it is then combined with external tools that are constantly learning and improving through feedback. It is highly beneficial in IT staff augmentation as well.
Autonomy: Autonomous behavior represents agentic AI. It’s special IT automation skill is to learn and work on its own makes it an ideal technology for companies seeking to simplify workflows and have machines do difficult tasks with minimum human effort.
Interactivity: Agentic AI has a practical nature through which it can deal with the outside and collect information to adjust in real-time. One of the best examples is self-driving vehicles.
Planning: Agentic AI models can manage difficult scenarios and perform multi-step techniques to accomplish specific goals.
Agentic AI and AI Agents
It is necessary to distinguish between agentic AI and AI agents. Importantly, agentic AI is a structure, while AI agents are the building blocks within the framework.
Agentic AI is a vast concept that solves problems with the least human supervision. While AI agent is a unique component within that system that is developed to manage tasks and processes with a level of autonomy. This special model is transforming the way humans use AI. The agentic AI model is capable of understanding the goal or vision of the user and implementing the information that is given to solve the issue.
Use Cases for Agentic AI and Generative AI
There are various use cases for generative AI, while a lot of applications of agentic AI are still in the experimental stage. Agentic AI use cases are evolving in roles such as customer service, healthcare security, workflow management, and financial risk management.
Generative AI Use Cases
Content Creation for SEO
Businesses are using generative AI to craft huge amount of SEO-optimized content, including blogs as well as landing pages that help boost organic traffic. For example, a digital marketing company might use generative AI tools to create top-quality and SEO-optimized blogposts or webpages for their clients to rank higher on search engines.
Marketing and Sales
Human sales teams often get slowed with administrative tasks when their actual goal is to search and develop sales leads. Sales teams have been working as generative AI for a while now through chatbots as well as virtual assistants. The AI technology can perform various tasks and boost optimization on a sales team In addition to lead generation outreach.
Product Design and Development
The generative AI can assist organizations in crafting new product ideas or templates based on market research, trends, and user preferences. This may boost the speed of the product development cycle.
Customer Support Automation
Using generative AI companies can automatically generate answers for customer service questions. This tool can help give answers to common questions as well as troubleshoot issues in real time. For example, take an e-commerce business use generative AI In chatbots to handle tasks, including order status inquiries, refund request, and shipping questions.
Agentic AI Use Cases
Customer Service
The traditional tools for customer chatbots had limited approach because of the pre-programmed nature of the technology which would require human effort at times. While with autonomous agents, the tool can easily understand what the customers intent and emotion is, and can take steps to resolve the problem.
These autonomous systems can therefore access a situation beforehand to make sure that the customer has an easy interaction with a business. Also, agentic AI can manage time consuming tasks by collecting, cleaning, and structuring an organizations data. The systems can take the workload of human employees and handle their high impact projects and tasks.
Healthcare
AI technology has already been used in the healthcare field, such as diagnostics, patient care, and simplifying administrative tasks. Furthermore, one of the most important features of AI tool is cybersecurity that is specially used in the healthcare field.
Automated Workflow Management
Financial Risk Management
Agentic AI helps company’s complete client objectives and enhance the results in real-time by researching market trends and financial information. This helps make separate decisions about funds as well as credit risks.
Agentic AI helps enhance these practices by working independently and managing strategies according to real-time economic, social, and political events.
Conclusion
Both agentic AI and generative AI have their own benefits. They showcase two effective and unique advancements in the field of Artificial Intelligence. Generative AI crafts content, such as images, videos, and text. On the other hand, agentic AI focuses on decision-making and taking action. True Refined Solutions (TRS) is the best platform that offers top-level IT automation services to all businesses.