When ChatGPT launched, it had taken the world by storm. What started as a tool to boost productivity witnessed exponential adoption, with 300 million weekly active users today. Soon, tools like Copilot emerged, transforming workforce productivity like never before. 

Yet, since change is the only constant in the world of technology, tech stalwarts quickly began to realize the inherent challenges of Gen AI models. From dependence on human input to the restricted ability to make decisions, leading to the emergence of Agentic AI! 

This blog will take you through the differences and similarities between GenAI and Agentic AI. It will help you get a clear understanding of what they aim to accomplish and in which use cases or real-world applications they can be used. 

Understanding Gen AI

Generative AI, a type of Artificial Intelligence (AI) technology, enables humans to create new text, audio, code, images, and other content. Using large language models (LLMs) and a host of other deep learning and natural language processing algorithms, Gen AI models can produce a variety of creative outputs, enhancing productivity and efficiency. 

• Gen AI models are popularly used to create new and creative content. From writing essays to code, image generation, and even music composition – Gen AI can produce novel ideas while improving content quality. 

• In addition to being creativity-oriented, Generative AI models can comprehend and summarize text. For instance, they can generate accurate minutes of meetings after a team discussion, list action items from an email, and summarize voice calls. Gen AI tools also have indexing and searching abilities, directing users to the information they need in long-form content. 

• In addition to text, GenAI can understand and generate outputs across multiple types of data including images, audio, and video. This multi-modal nature of generative AI opens several new use cases such as realistic virtual reality simulations, automatic video analysis, or personalized learning experiences in educational software.

• However, Gen AI models are human-dependent. To generate the correct outputs, they require user prompts. This means they aren’t autonomous and cannot make decisions on their own.  

• Example: In website building, a Gen AI-based model can take user prompts to create content for each page, suggest or review code, and debug issues. It can also generate custom layouts, suggest intuitive user interfaces, and create SEO-friendly content tailored to audience preferences. 

Understanding Agentic AI

In contrast to GenAI that depends on human input to generate an outcome or deliver results, Agentic AI is an automation platform that incorporates Artificial Intelligence capabilities with a large degree of autonomy to carry out tasks from beginning to end. It can make decisions, plan actions, and react to new changes in real-time. Unlike traditional AI models that depend on human prompts to execute predefined tasks, AI intelligent agents can make decisions, plan actions, and even learn from their experiences without human intervention. 

• AI agents can handle complex tasks through deep reasoning. These tools can adapt their approach based on changing circumstances by breaking them down into smaller, manageable steps. 

• Agentic AI is outcome-oriented and persistently works toward a defined business target to achieve a specific outcome. 

• One of the most compelling capabilities of an AI agent is its autonomy. It can act independently, take independent actions without needing supervision or detailed instructions, perform end-to-end processes, and achieve goals.  

• Example: In website building, AI agents can develop the entire website structure, including screen layouts, visuals, content placement, etc. It can also generate content for each page, write necessary backend code, and test the site for responsiveness. 

Gen AI and Agentic AI Differences 

Gen AI and Agentic AI are also different in many ways: 

The Way Forward

The tech world is constantly evolving. And just when we had begun to get comfortable with Gen AI, another innovative technology emerged. Agentic AI has brought about a paradigm shift in the world of Artificial Intelligence, known for its autonomous decision-making capabilities and self-learning adaptability.

GenAI depends on human prompts to act on data and deliver outcomes; Agentic AI on the other hand can perform tasks autonomously. While GenAI models use rule-based techniques, Agentic AI models depend on reinforced learning techniques to deliver the most optimal results. 

These differences make GenAI and Agentic AI different and suitable for different use cases. Thankfully, there is no pressure of an either-or regarding these models. Depending on the business need, organizations can either implement a Gen AI model for content summarization/creation or use AI intelligent agents to analyze data, set goals, create plans, perform tasks, make decisions, and learn from their experience to improve decision-making.