Topics In Demand
Notification
New

No notification found.

LEADER TALK: IN CONVERSATION WITH Ramakrishnan Venkatasubramanian, Chief Digital Officer, Bahwan CyberTek
LEADER TALK: IN CONVERSATION WITH Ramakrishnan Venkatasubramanian, Chief Digital Officer, Bahwan CyberTek

February 20, 2025

17

0

"We’re witnessing a transformative shift from ‘AI as a helper’ to ‘AI as a doer.’ Agentic AI doesn’t just generate insights - it drives decisions, adapts in real-time, and navigates complexity autonomously."

The world of Generative AI (GenAI) has evolved at unbelievable speed. Now, we have Agentic AI. What is ‘real’ Agentic AI and how should it be understood in comparison to the still-evolving GenAI?

 

There are incredible changes happening at the moment. Generative AI, which was seen as a giant leap for AI in the last few years, is slowly fading into the background. Right now, we’re witnessing a new trend, a transformative shift from ‘AI as a helper’ to ‘AI as a doer’. Gone are the days when AI was merely a tool for assisting. What we’re seeing today is a new breed of AI, one that is proactive, autonomous, and fully integrated into business decision-making. Agentic AI doesn't just generate insights, it drives decisions, adapts to dynamic business needs in real-time, and navigates complexity on its own.

 

This is the age of Agentic AI, where AI goes beyond assistance to become the driving force behind enterprise operations. Imagine AI systems that don’t just react but actively influence outcomes, guiding the enterprise through uncertainty, complexity, and change. AI agents are like sophisticated virtual assistants with the capabilities to handle complex workflows. This evolution represents more than just a technological upgrade, it’s a strategic paradigm shift.

 

Why should companies invest in Agentic AI? How is it expected to accelerate technology adoption and resulting efficiencies within an enterprise?

 

The promise of Agentic AI is immense, and enterprises are taking notice. A report released in 2025 suggests that 77% of U.S. IT executives are prepared to invest in Agentic AI this year.  At its core, Agentic AI seamlessly integrates with existing enterprise systems to reduce manual efforts and enhance efficiency. By analyzing vast amounts of data across multiple platforms, AI agents can deliver transformative outcomes, such as improving customer and employee experiences, accelerating go-to-market strategies, and optimizing product development.

 

The long-term potential is equally compelling. For instance, in drug discovery, Agentic AI can fast-track R&D by automating data analysis, modeling, and simulations. In risk management, it proactively identifies threats and executes mitigation strategies.

 

There are a wide range of enterprise applications ranging from software development to cyber security for agentic AI. However, like every other digital transformation project, the key is not just in investing in agentic AI but understanding how it will add value and thus increase revenue for businesses.

 

We have lately seen several large language model versions with bigger context windows and layered reasoning for better outputs, aspects crucial for AI agents to work. What other AI innovations are converging to make Agentic AI possible?

 

Generative AI, powered by Large Language Models (LLMs), was a game-changer. LLMs helped businesses process data, generate content, and automate routine tasks. But these models still relied on human input to function. The next leap is Large Action Models (LAMs), which are the action-oriented counterpart to LLMs.

 

While LLMs focus on interpreting and generating content, LAMs execute tasks autonomously—whether it's filling forms, processing refunds, or triggering workflows. This evolution represents AI’s transition from passive assistance to active decision-making and execution.

 

Although LAMs are still emerging, their role in decision-making and execution is clear. By combining LLMs and LAMs, businesses can now move beyond simply using AI for insights and automation. Instead, they empower autonomous, agentic AI to drive results, reduce human intervention, and optimize business processes.

 

Software development emerged as one of the most potent areas of GenAI-led productivity enhancements. How do you think Agentic AI could be used to further automate the SDLC and unlock process efficiencies?

 

In software development and engineering, AI is evolving into a fully autonomous coding partner, handling everything from code generation to testing and debugging. Agentic AI can be used to generate platform-ready code automatically, and tailored to specific business needs. Autonomous agents can create test scripts, automate testing, identify edge cases, refactor and reduce technical debt with minimal human intervention.

 

In the field of managed IT services, AI agents can revolutionize IT management by autonomously handling security, maintenance, and optimization tasks. AI agents can detect potential failures, initiate preventive measures, and automate troubleshooting and support tasks.

 

Agentic AI is nascent but is growing rapidly as the potential use cases are vast and practically universal. What in your view would be some of the most optimal use cases of Agentic AI in the next 2-3 years?

 

Agentic AI is set to revolutionize multiple industries by autonomously executing complex workflows, adapting in real time, and driving decision-making. Here are some optimal use cases for Agentic AI in the next 2-3 years:

 

Elaborating  more on the software development paradigm some of the direct yet impactful use cases can be as follows

  • Autonomous DevOps & CI/CD Pipelines – AI-driven build, test, and deployment without human intervention.
  • AI-Powered Code Engineering – Automated bug fixes, real-time code refactoring, and self-optimizing software.
  • AI Copilot Evolution – Beyond code suggestions, AI copilots will actively build and maintain software.

 

Further when it comes to broader IT use cases, Agentic systems can drive fully AI driven IT support/customer support managing complex tickets/queries and hyper personalise the user experience for IT managed services. Intelligent cloud orchestration where AI ops is run by agents instead of experts. Further cybersecurity is another area where a lot of monitoring, threat detection and response can be rendered autonomously.

 

From an industry point of view, there is quite a lot of opportunity across industries where there is AI penetration happening already. For example, in Healthcare and Life Science autonomous agents optimize research, run simulations, and speed up trials. In Financial Services & Risk Management, Agentic AI autonomously monitors transactions for anomalies and blocks fraud in real time.

 

The next 2-3 years will see Agentic AI becoming an indispensable force, enabling businesses to move beyond automation into autonomous decision-making

Community by nasscom Insights is focused on building the largest online community catering to the Indian technology sector. The purpose of the community is to bring the latest trends and discussions onto a single platform. Our passion for tech drives the free-flowing exchange of ideas and visions from industry leaders and game-changers across India.

© Copyright nasscom. All Rights Reserved.