Beyond the buzz of generative AI lies the next frontier: agentic AI. These autonomous systems don't just answer prompts; they complete complex tasks. This evolution is set to become a major force, potentially reshaping the tech services landscape.
Beyond Prompts: What Is Agentic AI?
First,
there were AI models that could classify data. Then came generative AI, like ChatGPT, which could create new content based on a prompt. Now, the conversation is shifting to agentic AI. Think of it as the difference between giving a chef a recipe and asking them to cook you dinner. Generative AI writes the recipe; an AI agent goes into the kitchen, gathers the ingredients, and prepares the meal. These are semi- or fully autonomous systems designed to pursue a goal with minimal human intervention. They can perceive their environment, reason, plan, and execute multi-step actions across different software applications to achieve a specific outcome. For example, instead of just generating a sales report, an agentic system could be tasked with 'increasing sales in the north region,' which might involve analyzing data, identifying leads, drafting and sending emails, and scheduling meetings autonomously.
The Engine of a New Growth Cycle
While some fear AI will shrink the tech services market through automation, many analysts predict the opposite. The rise of agentic AI is expected to create significant new value and expand the overall market. Projections suggest the enterprise agentic AI market could surge from just a few billion dollars in 2024 to over $46 billion by 2030, growing at a compound annual growth rate (CAGR) of around 47%. This growth won't come from simply replacing human tasks but from creating entirely new service lines and capabilities that were previously impossible. According to consulting firm BCG, agentic AI could unlock up to $200 billion in net new value pools within the next five years as it scales across enterprise functions. The demand is shifting from basic IT support and maintenance to strategic services like designing, deploying, and managing these complex, autonomous AI systems.
How Agents Will Reshape Tech Services
Agentic AI's impact will be felt across the entire tech services spectrum. In IT operations, agents can autonomously handle tasks like incident triage, resource management, and resolving up to 60% of common Tier 1 support tickets like password resets without human involvement. This moves the model from a reactive 'human-in-the-loop' system to a proactive 'human-on-the-loop' one, where people provide oversight rather than performing the task itself. In software development, agents can accelerate coding, automate testing, and even fix bugs. For business processes, agentic systems can automate complex workflows in finance, HR, and supply chain management, handling everything from fraud detection to employee onboarding. This allows human workers to focus on more strategic, judgment-based work that AI cannot yet perform.
A New Playbook for Indian IT
For India's massive technology services industry, agentic AI presents both a challenge and a monumental opportunity. The traditional model, based heavily on labour arbitrage and executing client-defined tasks, will face pressure as automation handles more low-level work. However, this disruption forces a necessary evolution up the value chain. The opportunity lies in becoming the architects and orchestrators of these new agentic systems for global clients. Indian IT firms are well positioned to pivot towards higher value services, including AI strategy, designing multi agent architectures, governance, and ensuring the security of these autonomous systems. It will require a significant shift in talent, moving away from a pyramid model with many junior execution roles toward a diamond shaped model with a strong core of highly skilled experts who can manage both people and AI agent workforces.
Navigating the Road to 2030
The path to widespread adoption is not without its hurdles. According to Gartner, agentic AI is currently at the 'Peak of Inflated Expectations,' with ambition often outpacing execution. While many companies plan to invest, few have achieved scaled deployment. Key challenges include ensuring governance to prevent 'rogue' agents, managing security risks, and controlling costs. Enterprise adoption will also be slower than consumer facing AI, as businesses must carefully test and integrate these systems with existing workflows and compliance requirements. The greatest return on investment is expected to come not from general purpose agents but from specialized , domain specific ones trained for particular business processes. Success will depend on building trust and developing the new skills needed to manage an increasingly autonomous digital workforce.
