Amazon and Visa announced a partnership to develop agentic tools that transform how consumers shop and pay online.
The two giants plan to offer software development resources through Amazon’s web services marketplace, enabling developers to create autonomous AI-powered shopping experiences that let ai agents transact on behalf of consumers.
Agentic commerce is moving from concept to reality faster than most anticipated. Here are six ways to prep your business, turning AI agents into automated revenue machines.
AI Agents and How They Works
Agentic commerce uses advanced AI systems, known as agents, to act autonomously on behalf of users. Unlike traditional AI tools that simply provide recommendations, AI agents work by observing their environment, reasoning about decisions, and taking action without constant human oversight.
These systems leverage machine learning algorithms, enabling them to make decisions based on data patterns. AI agents work through a continuous cycle:
- they collect data through sensors or digital inputs,
- process that information using algorithms,
- and then performs tasks based on what they’ve learned.
Each time an agent runs, it repeats this process, checking the environment again, deciding what to do, and acting accordingly.
The technology behind AI agents combines neural networks and reinforcement learning, which empowers them to adapt and enhance their performance over time. By analyzing large volumes of customer data, these systems identify patterns, predict outcomes, and offer recommendations tailored to specific needs in real time.
Agentic Commerce Models
Agents operate through three primary models that define how they interact with businesses and complete transactions.
The first model involves direct interaction where a personal shopping agent communicates directly with a retailer’s systems to browse products, add items to carts, and complete purchases.
The second model features agent-to-agent transactions, where a consumer’s personal shopping agent communicates with a retailer’s in-house AI commerce agent to negotiate bundle discounts across items in different departments. This creates a more dynamic shopping experience where both sides use AI to optimize outcomes.
The third model uses intermediary systems that facilitate multiagent and multiplatform interactions. For example, a restaurant-booking agent contacts the broker agent of a platform like OpenTable, which finds available tables and applies loyalty discounts based on user profiles.
These agents make autonomous decisions by assessing multiple factors in real time, including inventory availability, pricing across competitors, customer preferences, and delivery options.
INCORPORATE AI INTO YOUR BUSINESS
6 Ways Merchants Can Adopt Agentic AI Into Their Business Practices
Business owners ready to embrace agentic commerce can start implementing today. Here are 6 ways to start using AI agents and prepare for the future of commerce.
1. Automate Product Discovery So Agents Can “Find You”
When AI agents shop on behalf of customers, product discovery starts long before a human lands on your site. To capture that intent, you need to make your business easy for agents to understand and consume.
Key moves:
- Structure product data with clean titles, attributes, and categories so AI systems can parse and compare you accurately.
- Expose product feeds and APIs so agents can query inventory, pricing, and availability in real time.
- Tag products with rich metadata (use cases, compatibility, care instructions) so AI agents work with more context and are more likely to recommend you.
This groundwork lets agentic commerce experiences surface your catalog as a relevant option instead of skipping over you for better-structured competitors.
2. Let AI Agents Handle Repetitive, Revenue-Critical Tasks
Most teams lose margin to repetitive tasks that don’t need human brains but still impact revenue if they’re slow or inconsistent.
Use agentic AI to:
- Automate routine tasks like sending back-in-stock alerts, order status updates, and subscription renewal nudges.
- Proactively recommend add‑ons or upgrades based on customer data and historical buying patterns.
- Monitor carts and browsing behavior in real time and trigger well-timed prompts, discounts, or bundles to rescue at‑risk sessions.
This frees your team to focus on higher-value work while agents quietly perform tasks that lift conversion and average order value.
3. Turn Customer Service Into a Sales Channel
Customer service doesn’t have to be a pure cost center. With the right agentic AI setup, support can actively drive revenue while still improving satisfaction.
Practical plays:
- Deploy agents on chat and messaging channels to answer common questions instantly and reduce wait times.
- Train those agents on product data, policies, and customer history so they can make tailored recommendations, not just generic replies.
- Route complex or high-value cases to humans, but let agents pre-qualify needs, gather context, and suggest solutions so agents operate as smart front‑line triage.
Handled well, every “Where’s my order?” or “Does this fit?” interaction becomes a chance to upsell, cross‑sell, or save a potential churn.
4. Use Agentic AI to Optimize Pricing and Promotions
Agentic commerce thrives on fast, data‑driven decisions—exactly where machine learning shines.
Ways to apply it:
- Run dynamic pricing models that adjust based on demand, inventory levels, and competitor signals while respecting your guardrails.
- Have AI agents identify patterns in discount performance, customer segments, and channels so you don’t overspend on promotions that don’t move the needle.
- Use real‑time insights to tailor offers at the session level—think personalized bundles, loyalty boosts, or limited‑time incentives for hesitant shoppers.
By letting AI agents work on the “math” of pricing and offers, you protect margin while still giving customers compelling reasons to buy now.
5. Make Your Commerce Stack “Agent Ready”
To turn AI agents into reliable revenue, your systems need to support end‑to‑end, low‑friction transactions, not just cute demos.
Focus on:
- APIs for catalog, pricing, inventory, loyalty, and checkout so external and internal agents can perform tasks without brittle screen‑scraping workarounds.
- Clear rules and guardrails: spending limits, approval thresholds, allowed payment methods, and escalation paths to human oversight when something looks off.
- Payment flows designed for agents: tokenized cards, saved payment methods, and strong authentication that still allows secure, autonomous payments.
This is the technical backbone that lets agents operate safely in the real world, not just in slide decks.
6. Experiment With New Agentic Revenue Models
Once the basics are in place, you can use agentic AI to create new value, not just optimize existing funnels.
Ideas to explore:
- Vertical “expert” agents (e.g., a skincare consultant, a parts finder, a travel outfitter) that live on your site or in partner ecosystems and guide customers from vague intent to fully built baskets.
- Subscription or membership programs where AI agents manage replenishment, upgrades, and personalized offers automatically, reducing churn and boosting lifetime value.
- Data-driven services that analyze aggregated, privacy‑safe agent interactions to inform merchandising, product development, and campaign strategy.
These models turn AI agents from background utilities into branded experiences that differentiate your business.
The Future Arrived Faster Than You Thought
The Amazon-Visa partnership proves that industry leaders are investing heavily in agentic infrastructure, accelerating the timeline for when these tools become mainstream. Digital payments companies including PayPal, Fiserv, Stripe, and Mastercard are all racing to make bot-shopping a reality.
While challenges around security, returns, and accountability remain, the momentum behind agentic commerce is unstoppable. Merchants who view it as an opportunity rather than a threat will thrive in this new environment.
Start using AI to solve problems, boost revenue, and retain customers.