The Rise of Agent-to-Agent Transactions: Is Your Commerce Stack Ready?

What are AI Shopping Agents?

Shopping agents are autonomous software agents, often powered by large language models (LLMs), that can interpret user goals, make decisions and carry out multi-step transactions on the user’s behalf. These agents can:

• Receive natural language instructions (e.g., “Find me a flight and a hotel under $1,000 for Labor Day weekend”).
• Conduct research and evaluate trade-offs between options using preferences, constraints and historical behavior.
• Interact with third-party systems via APIs to search, compare and transact.
• Learn and adapt based on feedback.

Of course, this requires human users to grant their buy-bots access to payment information and give them permission to place orders on their behalf either fully autonomously or through notifications and one-tap approvals under specific conditions. Today’s consumer is likely not ready for this entirely, but they’re showing signs of warming up to it.

While as of this writing autonomous shopping bots are still in the conceptual phase, at the pace the space is moving, they could be commercially available at any moment through a consumer interface like ChatGPT, Google's Search Generative Experience or Apple’s upcoming Apple Intelligence or via third party mobile apps and services that emerge as dedicated shopping tools.

We also anticipate that first-moving brands will also explore embedded agentic capabilities directly in their owned channels. For example, customers could authorize autonomous purchases when products meet predefined criteria—such as falling below a target price or reaching a specific discount threshold—or allow the system to proactively build wish lists and shopping carts on their behalf.

Why Consumers Will Adopt Them (Fast)

While today this concept sounds scary, it’s not unreasonable to believe consumers will adopt agentic shopping AI if it proves to make their lives easier. Consumers are overwhelmed by choice, fatigued by decision-making and increasingly open to assistance — especially when it saves time and feels personal.

Several converging trends make the adoption of agentic shopping assistants not only plausible but inevitable:

1. Consumer Comfort with LLM-Powered Assistants

AI assistants like ChatGPT, Claude and Gemini have introduced the public to conversational interfaces that “think” and “do.” As these tools connect to more plugins, shopping becomes a natural extension.

2. Task Delegation as a Lifestyle

Busy consumers are already accustomed to outsourcing — from hiring virtual assistants to relying on curated subscription services. Shopping agents are a digital extension of this impulse.

3. Zero-UI Experiences

Voice interfaces (e.g., Siri, Alexa), wearables and car dashboards reduce screen-based browsing. Instead, consumers issue commands and expect agents to take care of the rest.

4. Invisible Automation

Much like autopay or smart thermostats, well-designed agents are “set-it-and-forget-it” — they manage replenishment, upsell when relevant and intervene only when necessary.

In short: consumers will gravitate toward agents not because they’re trendy, but because they offload cognitive load. Once trust is established, preference will shift from browsing to delegating.

From Customer Journeys to Agent Journeys

Traditionally, ecommerce teams design human-centric customer journeys: landing pages, navigation flows, cart funnels, recommendation modules. These are optimized for human perception and interaction.

But agentic AI flips this model. Agents don’t “see” a page the way a person does. They navigate via APIs, structured data and machine-readable interfaces. Their journeys are API calls, not visual pathways.

This creates two parallel worlds:

• Human journeys where users click, scroll, filter and compare.
• Agent journeys where bots query product catalogs, evaluate fit, check availability and trigger transactions.

The challenge for ecommerce leaders is to support both, without compromising either.

What Changes for Ecommerce Technology

To support agentic commerce, brands and retailers will need to revisit the plumbing and presentation layers of their stack.

1. Rich, Consistent and Machine-Readable Product Data

Agents rely on structured product metadata to make informed decisions. Incomplete specs, inconsistent tagging or poor schema design break their ability to evaluate and compare.

This will push organizations to:

• Enforce better product information management (PIM) hygiene.
• Use semantic tagging and rich taxonomies.
• Expose data via well-documented APIs with clear schema.

2. Composable and Interoperable Backend Services

Agents may need to interact with many backend systems — search, pricing, inventory, shipping — seamlessly. To support this, brands must:

• Adopt modular, API-first architectures that expose services independently.
• Enable dynamic orchestration across systems without tight front-end coupling.
• Ensure agents can query, transact, and retrieve data without friction.

Also, implementing Model Context Protocol (MCP) will be key. MCP acts as the “USB-C for AI agents,” allowing them to programmatically access catalogs, check stock, and place orders. This amplifies the value of composable commerce by making it agent-ready.

3. Agent-Friendly Front Ends and Search Interfaces

While agents prefer APIs, many will still rely on scraping or navigating semi-structured HTML. To help them, ecommerce sites should:

• Offer structured JSON-LD and schema.org markup.
• Avoid JavaScript-heavy content that delays or obfuscates loading.
• Consider offering agent-specific endpoints or bot-optimized search APIs.

This means search needs to become “dual-mode" — supporting rich natural-language understanding for users and lightweight, direct query access for agents.

4. New KPIs and Measurement Models

Traditional metrics like bounce rate or session time won’t apply to agents. Instead, teams will track:

• API consumption and latency
• Agent conversion rates
• Transaction volume from autonomous sources
• Agent-specific errors or breakdowns

In many cases, these journeys will be invisible, meaning observability tooling and event-based tracking become critical.

How Composable Commerce Enables Agent-Readiness

Composable commerce refers to architectures where functionality is built from independent, interchangeable services (search, pricing, cart, CMS), all stitched together via APIs.

This model is inherently agent friendly. Why?

• Agents need APIs, not pages.
• Dynamic orchestration lets brands respond in real-time to agent needs (e.g., showing price tiers, adjusting bundle logic).
• Personalization and pricing engines can expose context-aware offers at the API layer.

This means composable commerce is a prerequisite for agentic ecommerce, because it lets retailers expose only the necessary logic and data to different audiences — humans, bots or brand-owned agents — without duplicating infrastructure.

Even more powerfully, you can tailor API behavior per agent. For example:

• Offer different recommendation algorithms to consumer-facing agents vs. internal fulfillment bots.
• Rate-limit or authenticate third-party agents to preserve infrastructure health.
• Trigger human intervention only when agents encounter out-of-policy decisions (e.g., edge-case returns).

This granular control is impossible in monolithic platforms but trivial in a composable model.

Looking Ahead: A World of Agent-to-Agent Commerce

In the future, we won’t just see customers sending agents to stores. Brands will also deploy their own sales agents — AI that negotiate, upsell or cross-sell in real time.

This leads to agent-to-agent commerce:

• A consumer’s shopping agent interacts directly with a brand’s merchandising agent.
• The two negotiate quantity discounts, compare price options or collaborate on bundles.
• Transactions happen without the consumer or brand staff directly involved.

This is not far-off science fiction. As LLMs gain reasoning and goal-orientation, B2C agent networks will become the norm.

For example:

• A consumer tells their assistant: “I want a birthday gift for my brother who loves coffee and has a minimalist style. Budget: $100.”
• The assistant queries a dozen merchant agents, evaluates SKUs, compares delivery times and transacts.
• The retailer’s backend never shows a PDP. Instead, the agent returns an SKU, offer terms and payment API.

Preparing Now

Agentic ecommerce is coming faster than many expect. To prepare, ecommerce leaders should:

• Audit your APIs: Are they structured, documented and agent-friendly?
• Enrich your product data: Think like a bot—does your product catalog contain everything an agent would need to make a choice?
• Embrace composability: Ensure your tech stack is set up to connect with any new AI-driven systems and touchpoints that emerge in the next 1 to 5 years.
• Observe emerging behavior: Look for early signals of agent-driven traffic, even if minimal. Learn from it.
• Talk to your SI: Technology partners like Infosys Equinox are already building agentic commerce solutions for global brands. A strong partner can guide you on composability orchestration and security guardrails.
• Identify high-impact AI use cases: Focus on where agentic AI can drive the most business value. Prioritize use cases that align with your strategic goals and can be scaled across channels.
• Establish trust and governance: Agentic systems must act transparently, ethically, and securely. Build trust through explainable agent actions, strong data governance, privacy regulations and defined guardrails.

Partnering for the Agentic Era

Agentic AI isn’t a novelty — it’s a paradigm shift. The brands that prepare for agent-first commerce now will earn outsized loyalty and efficiency later. The journey begins not with redesigning your storefront, but with rethinking how machines experience your commerce stack.

Infosys Equinox can be your strategic partner in this transformation. With composable, API-first architecture and advanced agentic AI capabilities, Infosys Equinox empowers enterprises to build intelligent, secure, and scalable ecosystems tailored for autonomous commerce. From enabling invisible agent journeys to supporting agent-to-agent negotiations, our platform can help your brands lead in this new era.

Are you ready to lead in the agentic commerce era? Let Infosys Equinox help you build the future. Write to us at contactus@infosysequinox.com.