Agentic Commerce: Redefining Human-in-the-Loop | Mint

Digital commerce has long been designed around one core idea: keep humans in control while making every step faster and easier. Search got smarter, checkouts got shorter, and payments became nearly invisible. But a new phase is emerging—one where AI agents do more than assist. They can increasingly act on a shopper’s behalf.

This shift is known as agentic commerce. Instead of users manually completing each step, they may pre-authorize an AI system to search for items, compare options, fill carts, and even complete purchases within clear boundaries. The result could be a more efficient and highly personalized commerce experience. But it also changes a foundational assumption of online shopping: that the human is present for every key action.

That is why the next stage of digital commerce will depend less on raw automation and more on trust architecture. If an AI agent places an order for a consumer, the system must prove that the purchase reflects the user’s real intent, stays within approved limits, and can be audited later if something goes wrong.

From manual shopping to delegated shopping

Agentic commerce is developing along two tracks. In the merchant-led model, AI assistants are built directly into a retailer or service platform. A user might chat with a travel app, for instance, and let it recommend, shortlist, and book a trip inside the same interface. Indian platforms such as MakeMyTrip and ixigo are already experimenting with this kind of in-app conversational commerce.

The second track is the third-party agent model. Here, assistants such as ChatGPT or Claude can work across multiple merchants, compare offers, and execute portions of the shopping journey for the user. In India, companies like Swiggy and Zepto have opened parts of their systems to external AI agents through standards such as MCP, allowing those agents to search, manage carts, and place orders.

These models may coexist across apps, messaging platforms, voice interfaces, and third-party assistants. For consumers, that means shopping may no longer happen through just one screen or one store. For merchants, it means product discovery and payments must function in ecosystems they do not fully control.

Why payments become the real test

Automation in payments is not entirely new. Subscriptions, standing instructions, and auto-debit mandates already allow money to move without explicit approval every single time. But those systems usually operate within fixed rules: a known merchant, a known amount, or a set schedule.

Agentic commerce is more flexible—and therefore more complex. A user might instruct an AI agent to reorder groceries when essentials run low, buy a flight below a certain fare, or purchase an approved item if the price drops beneath a threshold. In such cases, the merchant, the product, and even the final amount may not be known at the time of authorization.

That creates a tension. If consumers must manually approve every purchase, much of the convenience disappears. But giving AI agents unrestricted access to a bank account or card is obviously unacceptable. The practical answer lies in controlled delegation.

What controlled delegation looks like

Controlled delegation means a consumer gives an AI agent permission to act, but only within clearly defined conditions. For example, a user could authorize an agent to purchase groceries up to ₹2,000 per order and no more than ₹5,000 in a month. The agent can operate freely within those guardrails, but not beyond them.

For this model to work at scale, four capabilities are essential:

• Accuracy: the product, merchant, quantity, and price must match the user’s instruction.
• Authentication: the system must verify the consumer when permissions are set or changed, and must verify that the agent is acting under valid authority.
• Control: users need simple ways to define limits, restrict categories or merchants, set time windows, and pause or revoke access.
• Auditability: every agent action must be traceable so disputes can be investigated and resolved.

This is the foundation of a modern human-in-the-loop system. The human is not removed from commerce; instead, their intent is translated into enforceable digital rules.

The rise of the AI agent as a new payments actor

Traditional payment systems are built around three main participants: the consumer, the merchant, and the payment credential. Agentic commerce introduces a fourth participant: the AI agent. That agent must be identified, authenticated, and linked to the user’s authorization.

For merchants, the questions become straightforward but critical: Is this a legitimate agent? Does it have real permission from the user? What exactly is it allowed to buy? Is this transaction still within approved limits? And if something fails, can the full chain of decisions be traced?

Supporting this across cards, wallets, real-time payments, and bank rails is not simple. Different payment methods handle credentials, authentication, and authorization in different ways. Merchants cannot realistically rebuild their stack for every new agent framework or commerce interface.

Why standards and orchestration matter

This is where protocols and payment infrastructure become central. Commerce protocols define how agents and merchants exchange information across search, checkout, and after-sales support. Emerging efforts such as Google’s Universal Commerce Protocol and the Agentic Commerce Protocol from Stripe and OpenAI are trying to standardize these interactions.

On the payments side, frameworks are emerging to verify agent identity and authorization. Visa’s Trusted Agent Protocol and Mastercard Agent Pay are examples of systems designed to support agent-led transactions with traceability, user permissions, and tokenized credentials.

But standards alone are not enough. The ecosystem also needs orchestration layers that connect merchants to agents, protocols, payment methods, and payment providers without forcing expensive rebuilds. Such infrastructure must remain interoperable so merchants retain flexibility while still applying rules consistently.

India’s opportunity in agentic commerce

India is particularly well positioned for this shift. Voice interfaces, local-language commerce, and messaging-led customer journeys are already widespread. Many businesses are exploring AI-driven experiences inside WhatsApp and other chat-based channels, allowing product discovery, order creation, and payment to happen in one conversation.

That opens the door to broader usage among the next wave of digital consumers—especially those who may prefer natural language or voice over app navigation. At the same time, third-party concierge agents could change how people compare prices, discover products, and make routine purchases across multiple merchants.

The bigger challenge: keeping humans meaningfully in control

Agentic commerce is not just about faster shopping. It is part of a larger world where AI agents, connected devices, and automated systems work together across digital and financial tasks. In that environment, one instruction can trigger a chain of actions. That raises the bar dramatically for security, accountability, and correctness.

The future will depend on whether systems can prove that machine actions truly match human intent—and do so within tightly bounded permissions. Human-in-the-loop design, then, is not just another confirmation screen. It is the framework that defines when machines may act, when people must intervene, and how responsibility is preserved.

If agentic commerce succeeds, it will not be because humans were removed from the system. It will be because technology found a reliable way to keep them in control, even when they are no longer clicking every button themselves.

Note: This article is a rewritten journalistic adaptation of a paid consumer connect piece originally published by Mint.