Agentic AI Growth: Pine Labs and Google Cloud Push AI-Powered Commerce in India
Agentic AI Growth is becoming an increasingly important theme across India’s technology ecosystem as startups and established companies explore ways to use AI agents for automation, commerce and business operations.
The latest example comes from Pine Labs and Google Cloud, which have partnered to develop AI-powered commerce solutions for Indian merchants. The collaboration combines Pine Labs’ payments and merchant infrastructure with Google Cloud’s artificial intelligence capabilities, including Gemini-based agent technology.
The companies are targeting areas such as product discovery, advertising, payments and merchant operations, reflecting a broader shift from AI that simply generates information toward AI systems that can perform tasks and take actions.
Agentic AI Growth Accelerates in Indian Commerce
Traditional AI applications often focus on answering questions, generating content or analysing information. Agentic AI takes the concept further by allowing software agents to plan and execute multi-step tasks with limited human intervention.
In commerce, this could mean an AI agent searching for a product, comparing available options, interacting with a merchant system and eventually initiating a transaction.
For Indian businesses, such capabilities could change how customers discover products and how merchants manage digital operations.
The Pine Labs and Google Cloud partnership is one example of companies building infrastructure around this emerging model.
Pine Labs and Google Cloud Target Automated Commerce
Pine Labs announced its collaboration with Google Cloud to advance agentic commerce in India, with a focus on helping merchants become more visible and accessible within AI-driven shopping experiences.
The partnership includes plans to use Google’s Gemini Enterprise Agent Platform for applications such as catalogue enrichment, search and customized commerce agents.
This could help businesses structure their product information so that AI systems can more effectively understand and surface their offerings.
The companies are also exploring AI-powered tools for advertising and merchant operations.
From AI Assistance to AI Action
One of the defining characteristics of agentic AI is the transition from assistance to action.
A conventional chatbot might tell a customer where a product is available. An AI agent could potentially search multiple merchants, identify suitable options and complete the next steps in a transaction based on predefined permissions.
This distinction is important for businesses because it moves AI from the customer-support layer into the actual commerce workflow.
For merchants, the opportunity could involve automation across marketing, product discovery, customer engagement and payments.
AI-Powered Advertising for Indian Merchants
Pine Labs is also developing an AI advertising agent designed to help merchants create and distribute digital advertising campaigns.
The system is planned to work across surfaces including Google Search, YouTube and Pine Labs point-of-sale devices.
For smaller businesses, automated advertising could reduce the complexity involved in creating campaigns, selecting audiences and managing multiple digital channels.
The technology does not eliminate the need for business decisions, but it could automate some of the repetitive tasks involved in digital marketing.
Agentic Payments Could Change Digital Transactions
Payments represent another important part of the agentic-commerce ecosystem.
Pine Labs’ Pine Labs Payment Protocol, or P3P, is designed to provide payment infrastructure for transactions initiated within agentic commerce experiences.
The company has said the protocol is starting with UPI before potentially supporting additional payment rails.
This type of infrastructure could become important if AI agents increasingly move from recommending products to actually completing purchases.
A successful agentic-commerce system needs not only product discovery but also secure authentication, transaction processing and appropriate controls around financial activity.
Google Gemini’s Role in Agentic AI
Google Cloud’s Gemini technology is central to its broader enterprise AI strategy.
In the Pine Labs collaboration, Gemini Enterprise Agent Platform is being used as the foundation for developing and deploying AI agents.
The approach allows businesses to build specialized agents for specific workflows rather than relying on a single general-purpose AI assistant.
For Indian technology companies, access to enterprise AI infrastructure could make it easier to experiment with applications across finance, commerce, customer service and business operations.
Indian Startups Explore AI Automation
The rise of agentic AI comes as Indian startups increasingly focus on automation.
AI is being incorporated into enterprise software, fintech, customer support, logistics, marketing, healthcare and other sectors.
The attraction for entrepreneurs is partly linked to the ability of AI agents to handle repetitive workflows while allowing employees to concentrate on tasks requiring judgment and oversight.
However, deploying autonomous systems also creates challenges involving accuracy, security, privacy, compliance and human supervision.
Why Indian Tech Entrepreneurs Are Watching Agentic AI
India’s large digital economy provides an environment where agentic commerce could be tested at significant scale.
The country’s widespread use of digital payments, particularly UPI, provides existing infrastructure for fast electronic transactions.
At the same time, millions of merchants are increasingly using digital platforms to reach customers.
This combination creates opportunities for AI systems that can connect product discovery, customer interaction and payments.
For entrepreneurs, the emerging market is therefore not limited to developing AI models. It also includes building the infrastructure that allows AI agents to interact with real businesses.
The Rise of AI-Native Commerce
Agentic AI could eventually create a different model of online shopping.
Instead of customers manually visiting multiple websites, searching for products and comparing prices, AI agents could potentially perform much of that work.
Merchants would then need to ensure their catalogues, pricing, inventory and transaction systems are accessible to AI platforms.
This creates a new form of digital visibility.
Search-engine optimisation has traditionally focused on helping websites appear in search results. In an agentic-commerce environment, businesses may also need to make their information understandable and actionable for AI agents.
Automation Beyond Customer Transactions
Agentic AI is not limited to shopping.
The same technology can be applied to internal business workflows.
An agent could potentially monitor inventory, identify unusual transaction patterns, generate reports, coordinate customer-service requests or assist with marketing operations.
Pine Labs is also developing AI tools for merchant operations, illustrating how the technology can operate behind the scenes as well as in customer-facing applications.
This broader application is one reason companies across multiple industries are investing in AI agents.
Challenges for Agentic AI Adoption
Despite the potential for automation, agentic AI still faces several challenges.
Accuracy is critical because an autonomous system making an incorrect decision can create financial or operational consequences.
Security is another concern. AI agents interacting with payment systems and business infrastructure require strong authentication and access controls.
Privacy and compliance become increasingly important when agents process customer or financial information.
There is also the question of human oversight. Businesses need mechanisms to define what an AI agent can do independently and when human approval is required.
What Agentic AI Growth Means for Indian Businesses
The development of agentic AI could change the way businesses think about automation.
Rather than using AI only for content generation or basic customer support, companies are increasingly exploring systems capable of completing entire workflows.
For Indian businesses, this could eventually mean automated marketing, AI-assisted sales, product discovery, customer service and payments operating within interconnected digital systems.
The Pine Labs-Google Cloud collaboration provides one example of how these pieces could come together.
The Next Phase of India’s AI Ecosystem
Agentic AI Growth is moving the conversation around artificial intelligence toward execution and automation.
The focus is shifting from what AI can generate to what AI agents can actually accomplish within business systems.
Pine Labs and Google Cloud’s work in agentic commerce illustrates this transition, particularly in a market where digital payments and online commerce are already deeply established.
As more Indian startups and enterprises experiment with AI agents, the next phase of the technology ecosystem is likely to involve greater integration between AI models, business software, payments and real-world commercial activity.
The pace of adoption will ultimately depend on factors including reliability, security, regulatory requirements, infrastructure and whether businesses can demonstrate measurable benefits from deploying autonomous AI systems.
Frequently Asked Questions
1. What is Agentic AI Growth?
Agentic AI Growth refers to the increasing adoption and development of AI systems that can independently perform multi-step tasks and workflows rather than simply generating information or responding to questions.
2. What is agentic commerce?
Agentic commerce is a model in which AI agents can assist with or perform activities such as product discovery, comparison, recommendations and transactions on behalf of consumers.
3. Why are Pine Labs and Google Cloud working together?
The companies are collaborating to develop AI-powered commerce solutions that can help Indian merchants with product discovery, advertising, payments and other business operations.
4. How is Gemini being used in the partnership?
Pine Labs plans to use Google’s Gemini Enterprise Agent Platform to develop customized commerce agents and applications involving catalogue enrichment and search.
5. What is Pine Labs P3P?
P3P, or Pine Labs Payment Protocol, is designed to enable payments within agentic commerce flows, with UPI as the starting payment rail.
6. How could agentic AI help small businesses?
AI agents could potentially automate tasks such as advertising, customer support, product discovery and certain operational workflows, reducing the amount of manual work required.
7. Why is India an important market for agentic commerce?
India combines a large digital consumer and merchant base with widespread digital payments and established infrastructure such as UPI, creating potential use cases for AI-powered commerce.
8. Is agentic AI limited to e-commerce?
No. Agentic AI can potentially be used across areas such as finance, marketing, customer service, logistics, healthcare, enterprise software and internal business operations.
9. What are the main risks of agentic AI?
Key challenges include accuracy, cybersecurity, privacy, regulatory compliance, financial controls and determining when human approval should be required.
10. What does agentic AI mean for Indian tech entrepreneurs?
It creates opportunities to build AI agents, enterprise infrastructure, payment systems and software that allow autonomous systems to interact with businesses and customers.
