Proactiveness. That’s how Enver Cetin, an AI expert at global Experience Engineering firm Ciklum, described Agentic AI in a Harvard Business Review article.

Agentic AI – the next big thing in the AI ecosystem the country will see in 2025, as Inc42 called it last December – is indeed driving us into yet another phase of transformative growth. And, all this is happening barely two years after Generative AI (GenAI) changed how industries function and individuals work, and the need to improve its resolution process and customisation was felt, accelerating the natural evolution of technology.

AI companies are actively building autonomous AI agents to help their enterprise and startup clients improve customer satisfaction. Many startups are also building in-house AI agents to help enhance the processing of ticket requests, reduce the need for human agents, and so on. From Gupshup to Zomato, Infosys to Zoho Corporation – Agentic AI has become a major focus for companies no matter how big or small they are or in which sector they operate in.

The rapid waves triggered by Agentic AI have fostered a $5.1 Bn market around the world, which is on course to surpass $47 Bn by 2030, averaging more than 44% growth rate. Reports suggest India has a high AI adoption rate, with some stating it accounts for around 40% of global AI agent deployments.

But, what is Agentic AI and how is it making companies smarter? We often see ‘AI agents’ and ‘Agentic AI’ being used interchangeably. Are they really the same?

Let’s understand the concept first.

What Is Agentic AI?

To put it simply, Agentic AI refers to autonomous GenAI agents that are capable of doing complex tasks with little or no human supervision.

These autonomous GenAI agents are more sophisticated in their ability to take actions, and hence, solve more complex tasks compared to what AI assistants could do. The earlier avatar could solve specific tasks such as writing emails or providing customer support, but the advanced version can handle complex tasks such as planning your next vacation, making travel arrangements, taking care of the elderly with human-like bots, and resolving supply chain issues by smarter management of inventories.

The foundation and the backbone remain the same where the open-source or closed-source large-language models (LLMs) are........

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