Every revolution in technology begins quietly, often in the background of systems we take for granted. Two decades ago, the engineering world struggled with debugging Internet Explorer and dismissed JavaScript as a “toy.” Today, those same lines of code have given way to intelligent agents that not only write software but also reason about business outcomes. We stand at the threshold of a new era; the era of agentic AI, where India has the chance to lead, not follow.

The shift is unprecedented. What began as prompt engineering only months ago has quickly matured into context engineering powering agentic workflows. Model Context Protocol (MCP) servers are enabling integration points that did not exist until recently. The acceleration is such that even seasoned technologists accustomed to disruption find the pace both exhilarating and unsettling.

When Traditional Approaches Hit the Wall

This acceleration became evident during a recent engagement with one of Australia’s largest national banks. The organisation operated on a 20-year-old monolithic system built on AngularJS, jQuery, and Java, a codebase few engineers were willing to touch. The original architects had long moved on, and with them went decades of tribal knowledge. Modernisation seemed impossible without risking regressions in systems that processed billions of transactions daily.

Traditional migration tools proved inadequate. Reverse-engineering decades of embedded business logic across JSPs, jQuery handlers, and legacy frameworks was not viable. Through the Slingshot platform, multi-layer context awareness was introduced to interpret not just what the code did, but why it had been written in a particular way. This approach went beyond automation, bringing in reasoning about business intent within complex technical constraints.

The real breakthrough came when agentic systems, working alongside seasoned architects, maintained continuity of context across every layer during the migration from AngularJS/jQuery/Java to React-based micro frontends. Human engineers remained in the loop, providing real-time feedback. For software handling billions in daily transactions, blind automation would have been reckless.

The Tool Trap: What AI Leaders Got Right

As Sam Altman has highlighted, organizations need advisors to ensure AI is used responsibly. However, the challenge extends further. Many enterprises are preoccupied with finding the “right AI tool” rather than focusing on the core business problems they need to solve.

This “tool-first” mindset is dangerous. It assumes that technology will automatically align with business needs, leading to overspending on pilots that never deliver strategic value. The real question is not “Which AI tool should be used?” but rather “What business outcome must be achieved?” AI is an enabler, not the solution in itself.

India’s Agentic AI Landscape: Where the Action Will Be

In India, three industries are positioned for the fastest adoption of agentic AI:

1. Financial Services: Indian banks and fintech firms have built digital-first infrastructure that rivals global institutions. With UPI, JAM trinity, and regulatory pushes for digitization, India offers fertile ground for agentic systems. Unlike Western banks encumbered by outdated payment rails, Indian fintech ecosystems are API-first, making integration smoother.
2. IT Services: Indian IT service providers are not merely adopting AI tools; they are redefining delivery models. AI-native service offerings, where human expertise and autonomous agents collaborate from the start, are set to reshape the industry’s global positioning.
3. Retail and E-commerce: Omnichannel platforms in India are being built fresh, unencumbered by legacy point-of-sale infrastructure. Quick commerce players have already laid the groundwork for agentic AI to manage inventory, predict demand, and optimize last-mile logistics. Conversational commerce via WhatsApp integrations further accelerates adoption, as Indian consumers are already comfortable with chat-based shopping.

Getting Enterprise Deployment Right

For enterprises, the path to deploying agentic AI effectively is clear:

1. Start with Engineering and DevOps: Productivity gains of 40–60% can be achieved while keeping risks low and metrics measurable.
2. Target repetitive tasks first: Code reviews, documentation, test generation, and infrastructure monitoring build trust before scaling into business-critical functions.
3. Move incrementally: Begin with human-supervised systems, demonstrate competency, then expand autonomy step by step.

India’s Unique Scaling Challenges

Indian enterprises face challenges not always reflected in global frameworks:

1. The talent paradox: While India produces large numbers of AI-trained professionals, few have enterprise-ready experience with legacy systems, compliance-heavy environments, and mission-critical deployments.
2. Infrastructure disparities: While metros enjoy strong digital infrastructure, manufacturing hubs often struggle with reliability. Edge computing and offline-capable AI agents can bridge this gap.
3. Cultural barriers: Hierarchical decision-making in Indian enterprises requires AI systems to be designed in ways that complement existing processes.
4. Regulatory constraints: Compliance with the DPDP Act, RBI guidelines, and sector-specific regulations is complex but creates opportunities to build globally exportable solutions.

Beyond Efficiency: The New Business Model Reality

The true transformation lies not in efficiency alone but in redefining business models. Globally, software companies are shifting toward outcome-based pricing, while some Indian IT giants still rely on time-and-materials contracts. The pressure is mounting, and models such as “pay per automated process” are beginning to emerge.

Hybrid workforce models are also gaining ground, where AI agents handle routine analysis and humans focus on higher-order problem-solving and client relationships. Rather than replacing offshore teams, this amplifies their value by freeing engineers from repetitive tasks.

Building Responsible Agentic Systems

Responsible AI in an enterprise context is an architectural requirement, not just a compliance checkbox. Success depends on three principles:

1. Transparency: Every autonomous action must leave an auditable trail understandable to both technical and business stakeholders.
2. Accountability: Human owners remain responsible for autonomous actions, ensuring context-sensitive interventions.
3. Human-in-the-loop: Explicit guardrails must define what agents can and cannot do autonomously, with continuous monitoring to prevent compounding risks.

The Engineering Leadership Imperative

After more than two decades of rapid change, software engineering has never faced a more exciting future. Success lies not in replacing human intelligence but in amplifying it; letting autonomous systems handle execution while humans focus on creativity and strategy.

Agentic AI must be treated as a core engineering capability, built with the rigor of any mission-critical system. For Indian enterprises, this is an unparalleled opportunity: while global firms retrofit AI into legacy systems, India can build AI-native operations on strong digital foundations, world-class talent, and cost advantages, unlocking leapfrog innovation.

The question is no longer if agentic AI will transform business, but whether organizations will lead that change or be disrupted by it. The future belongs to enterprises that seamlessly blend human expertise with autonomous capabilities, guided by leadership that embraces both the potential and the complexities of this revolution.