The Age Of Agents: Diving Into Indian Tech’s Agentic AI Moment In 2025

Last year, many expected agentic AI to become one of the major forces to reshape India’s tech and business landscape. And as we conclude this year, the impact is clearly visible. 

Enterprises are adopting multi-purpose AI agents to manage customer interactions, streamline sales and marketing workflows, and automate routine operational tasks that hinder team productivity.

Almost every major conversation around AI this year highlighted how companies are either building agent-driven workflows internally or enabling others to deploy agent-based solutions at scale. 

Now, as we bid adieu to 2025, let’s analyse which business functions underwent the most visible transformation, and what lies ahead for AI agents.

Speaking with Inc42, Ganesh Gopalan, cofounder and CEO of Gnani.ai, said that while early adoption of agentic AI was about experimentation without clear business metrics, businesses now have a sharp focus on ROI and end outcomes.

“In fact, Gnani.ai, which has been building language-based models, has become a new entrant in the agentic AI space, with its Inya.ai no-code platform, after recognising the scale of opportunity in multi-purpose AI automation,” Gopalan said.

In 2025, he added, a deep and rapid disruption occurred in customer experience and customer-facing processes. Let’s explore how…

How Customer Interactions Got A Makeover

For years, companies have desired to enhance customer experiences with minimal reliance on human teams, while still maintaining quality. 

Chatbots and automated self-service solutions helped. Still, agentic AI has dramatically accelerated this desire, offering systems that can understand context, solve multi-step problems, and engage more naturally with users.

In 2025, voice-based agents became particularly impressive, with lower latency, higher accuracy, and increasingly human-like tonality. As a result, adoption surged across large enterprises and digital-first brands.

Air India partnered with Salesforce to revamp its customer service stack with agentic AI. State Bank of India began integrating autonomous conversational workflows for customer interactions. Cars24 deployed a real-time voice intelligence platform using ElevenLabs’ multilingual models to analyse and guide customer-agent conversations in real time. 

AI-powered travel agents have also become mainstream, planning journeys and completing bookings end-to-end without human intervention.

Our interaction with multiple industry experts revealed that the adoption of agentic AI platforms, especially for voice-led interactions, is rising rapidly. 

Across marketing, onboarding, cross-sell and upsell, customer support, credit collections and accounts receivable, native AI companies are setting increased adoption rates. But AI agents are no longer just facing the customer — they are increasingly reshaping organisations from the inside out, helping employees execute faster and with higher consistency.

How AI Agents Are Supercharging Productivity

As customer-facing automation matured, enterprises realised that the biggest efficiency unlock was occurring in how employees accessed information, performed tasks, and made decisions.

Banking, insurance, ecommerce, recruitment, and even manufacturing companies are now deploying internal AI co-pilots that sit inside CRM dashboards, HR systems, enterprise knowledge bases, ERP modules, and communication platforms.

Axis Bank, for instance, now sees a 30% increase in product conversions across term deposits, mutual funds, and credit cards, along with a 10-point increase in net promoter score (NPS) with the help of GenAI-powered employee assistant, Siddhi.

A recent Deloitte study highlights how Tata Steel has begun using GenAI, paired with autonomous agents, for predictive maintenance. The result has reportedly been a sharp drop in unplanned equipment downtime and longer asset life — outcomes that, in a capital-heavy industry like manufacturing, directly translate into major cost savings and stronger operational resilience.

“The ability for AI agents to act like co-pilots and help augment human capabilities is being extensively tried out by enterprises across industries. It’s one of the biggest changes I have seen this year,” said Gnani’s Gopalan.

He also pointed out that human resources (HR) operations are increasingly getting automated with agentic agents. 

Deepak Gupta, group CTO and AI officer at AMPL Automotive Manufacturers, seconds the notion, saying that enterprises are increasingly looking to deploy agnetic HR business partners, agnetic chartered accountants, and such roles where employees can easily interact with and solve regular issues with the help of bots, giving more time to humans to perform more mission-critical tasks.

“And this is becoming true across most functionalities, such as underwriting in credit or insurance businesses, hiring of blue-collar workers, and car servicing in the auto industry, where companies have started deploying agentic inspectors.”

BFSI Rides The Strongest Tailwinds

Experts that Inc42 spoke with unanimously opined that the Indian BFSI space is reaping the most of the agentic AI wave. 

For the BFSI sector, the impact has been more nuanced, with effects extending to debt collection, underwriting of insurance, fraud detection, and more.

Shantanu Gangal, cofounder and CEO of US-based startup Prodigal, which is building AI agents for debt collections, said that loan servicing and collection today is quite a labour-intensive job. While front-office personnel interact with consumers and help them navigate any changes, heavy back-office tasks are performed in areas of valuation, pricing, accounting, compliance, QA, and others.  

“What might seem like an agentic AI revolution in the financial services sector, in reality, is a series of specialised agents acting and doing the revolution. We ourselves deploy a swarm of agents, such as fraud agents, dispute agents, texting agents, web UI agents, and voice agents. While they might seem very similar to the end consumers, the underlying orchestration is happening across multiple of these agents,” said Gangal.

The Build Vs Buy Hypothesis 

Whether in HR automation, sales enablement, or customer support, a majority of companies are choosing to adopt SaaS-based agentic solutions from specialist native AI startups rather than building from scratch.

AMPL’s Gupta said that many enterprises are largely operating in a hybrid mode today. “When a capability doesn’t offer deep strategic differentiation, companies deploy off-the-shelf AI agents and integrate them with internal data and systems,” he said. 

This approach reduces development time, accelerates workflows, and allows teams to show measurable results more quickly.

However, when the value lies in proprietary business intelligence – capturing nuanced customer behaviour, domain-specific workflows, underwriting logic, or product-level differentiation – large enterprises are increasingly choosing to build in-house.

Meanwhile, even as some companies are building agents and other AI capabilities in-house, they companies are dependent on platforms that are building LLMs, retrieval-augmented generation (RAG) database, AI agent framework, and other such core capabilities from scratch.

What Lies Ahead?

While GenAI triggered a massive technological reset, most systems were limited to answering queries, generating content, or providing recommendations until the emergence of agentic AI, which brought the ability for bots to perform multi-step tasks, make decisions, and execute workflows autonomously.

According to Gupta, the shift, now underway, is from informational AI to actionable AI. With AI agents increasingly capable of independent reasoning and task execution, the next wave will be about maturity, trust, and reliability. 

“With AI agents now capable of taking independent decisions and increasing emphasis on the ethical use of AI, there is a rising trend where AI is now able to explain why it reached certain conclusions,” he noted.

Meanwhile, industries that have multiple layers of functionality, including auto, logistics and supply chain, and ecommerce, are now on the verge of witnessing the next big shift with AI agents. 

While ecommerce giants like Amazon are already tinkering with AI agents, streamlining several aspects of the product-buying experience, the sentiment in the ecosystem is clear — soon, customers can shop end-to-end with AI agents.

Logistics, in particular, is expected to run far more smoothly with AI agents speeding up the flow of information across every part of the value chain — from wholesalers to showrooms and all the way to last-mile delivery executives.

The automotive industry is also betting big on AI agents for better market analysis, analysing the resale value of vehicles, and much more.

However, with such a high level of automation, mass layoffs will continue to remain a major bone of contention. Experts, however, opine that many new job roles will be created as a result of automation.

But challenges remain — from data security and reliability to governance, workforce adaptation, and standardisation. But companies that figure out how to embed agentic intelligence into their core processes, while managing the risks, stand to gain from here on.

Edited By Shishir Parasher