Agentic AI Takes Centre Stage: How Indian Startups Are Building Self-Directed Systems for Fintech, Enterprise, and Customer Service

For the past two years, the AI conversation in India has been dominated by a familiar pattern: a user types a prompt, a large language model (LLM) generates a response. The interaction is linear, reactive, and fundamentally human-directed. But a profound shift is underway.

Indian startups are increasingly embracing “agentic AI” —advanced systems capable of acting autonomously, not just responding to prompts. Unlike traditional machine learning models that require constant human input, agentic AI platforms can plan, execute, and adapt in real time, making decisions, taking actions, and learning from outcomes without continuous human oversight .

This evolution—from generative to agentic—is not just a technological upgrade; it is a fundamental rethinking of what AI can do. Instead of being a tool that answers questions, agentic AI becomes an active collaborator that solves problems.

What Is Agentic AI? A Shift in Paradigm

To understand the significance, it helps to distinguish between the two paradigms :

• Generative AI (Current): A model that processes a query and returns a response—a text answer, an image, a code snippet. It is powerful but reactive.
• Agentic AI (Emerging): A system with agency. It can:
  • Plan: Break down complex tasks into sub-tasks
  • Execute: Perform actions—sending emails, booking appointments, updating databases
  • Observe: Monitor the outcomes of its actions
  • Adapt: Adjust its approach based on new information or changing conditions
  • Collaborate: Work with other agents to achieve complex goals

Where a generative AI tool might write a draft email, an agentic AI system could analyze your inbox, prioritize messages, schedule meetings, draft responses, send them, and then follow up—all without human intervention .

This shift mirrors a broader global movement. As venture capitalist Sarah Guo noted, “We’re moving from intelligence in a box to agency in the world” .

Why India Is Poised to Lead in Agentic AI

Several factors make India an ideal proving ground for agentic AI.

1. Massive, Complex Service Sectors

India’s economy is built on services—banking, insurance, healthcare, IT, customer support—that involve complex, multi-step workflows. These are precisely the domains where agentic AI can deliver the most value. By automating workflows, reducing manual intervention, and improving decision-making, agentic AI can transform how Indian businesses operate .

2. A Talent Pool of Engineers and Product Builders

India has a deep pool of engineers who understand not just AI models, but also the business processes they need to automate. The same talent that built India’s global SaaS industry is now applying its skills to agentic AI .

3. A Culture of Frugal Innovation

Building autonomous systems requires careful orchestration of resources—computation, memory, and cost. Indian founders have a proven ability to build efficient, scalable systems with limited resources, a skill that is directly transferable to agentic AI .

Agentic AI in Action: Indian Startups Leading the Charge

While agentic AI is a global trend, Indian startups are among the earliest adopters and builders. Here are real-world examples across key sectors.

Fintech: Autonomous Fraud Detection and Compliance

Indian fintechs are deploying agentic AI to handle tasks that previously required human analysts.

• KreditBee uses AI agents for underwriting and fraud detection. The system doesn’t just flag suspicious transactions; it investigates them, cross-references multiple data sources, and makes decisions about whether to approve or decline in real time .
• Idfy, a verification and compliance platform, uses agentic AI to automate identity verification. The system checks documents, matches them against databases, flags inconsistencies, and routes exceptions to human reviewers only when necessary. This reduces manual effort while improving accuracy .
• Cheerio AI, a Bangalore-based enterprise automation startup, is building agentic platforms that automate customer communication, back-office operations, and internal workflows across BFSI, retail, and logistics .

Customer Service: End-to-End Resolution Without Human Intervention

Customer service is a natural fit for agentic AI. Instead of chatbots that answer simple queries, agentic systems can resolve entire issues.

• Ola’s Krutrim, India’s first unicorn AI startup, is building agentic capabilities into its platform. The system can handle complex customer service requests end-to-end—understanding the issue, checking relevant systems, initiating refunds, and confirming resolution—all without human intervention .
• Sarvam AI, which raised $41 million from Lightspeed and Peak XV, is building agentic systems for voice-first interactions across Indian languages. Its platform is designed to handle complex, multi-turn conversations in Hindi, Tamil, and other regional languages, making it ideal for customer service at scale .

Enterprise Automation: Workflow Orchestration and Decision-Making

Agentic AI is transforming how enterprises manage workflows.

• Zoho, the SaaS giant, has integrated agentic AI into its CRM and HR platforms. Zia, Zoho’s AI assistant, can now perform multi-step tasks—scheduling meetings, updating records, sending follow-ups—based on natural language instructions. The system plans the sequence of actions and executes them autonomously .
• Razorpay is building agentic AI into its payment platform. The system monitors transactions, detects anomalies, and can automatically initiate compliance checks, notify merchants, and even block suspicious activity without human input .

The Technology Stack: How Agentic AI Is Built

Building agentic AI systems requires more than just a large language model. It involves orchestrating multiple components .

1. Planning Engine: Breaks down complex goals into sequences of actions. This is often the most challenging part, requiring models that can reason about cause and effect.
2. Tool Use: The ability to interact with external systems—APIs, databases, email clients, calendars. This is what turns a language model into an agent that can act .
3. Memory: Both short-term (tracking ongoing tasks) and long-term (learning from past interactions) memory are essential for coherent, personalized behavior.
4. Observation and Feedback Loops: The system must be able to monitor the outcomes of its actions and adjust its strategy accordingly.

Indian startups are building these components from scratch or fine-tuning existing open-source models to create agentic systems tailored to Indian business contexts .

The Road Ahead: From Assistants to Collaborators

The shift to agentic AI has profound implications for how businesses operate.

• For Startups: It opens opportunities to build platforms that don’t just inform but actually do. The value proposition shifts from “here’s what you should do” to “let me handle that for you.”
• For Enterprises: It means reimagining workflows. Tasks that required multiple humans and days of coordination can now be handled by AI agents in minutes.
• For Talent: The demand for skills is shifting. Prompt engineering is giving way to workflow design, agent orchestration, and systems thinking .

As Ola’s Bhavish Aggarwal noted, the goal is to build AI that is not just intelligent but autonomous—capable of taking action in the real world.

The Final Word

India’s startup ecosystem has always been quick to adopt and adapt to new technologies. With agentic AI, it is not just adopting—it is helping to define the category. From fintech platforms that detect fraud autonomously to customer service agents that resolve issues end-to-end, Indian startups are building the systems that will define the next phase of AI.

The shift from generative to agentic is not incremental; it is transformative. It moves AI from being a tool that responds to a prompt to a collaborator that plans, acts, and learns.

For Indian startups, this is a moment of opportunity. The same skills—frugality, systems thinking, deep domain expertise—that built India’s IT services and SaaS industries can now be applied to building the agentic platforms that will power the next generation of business.

As the ecosystem continues to evolve, the startups that succeed will be those that don’t just build intelligent models, but build intelligent systems—systems that can work alongside humans, anticipate needs, and act autonomously to achieve goals.