How to Prepare for Agentic Commerce: The 9-Step Framework for AI-Driven Shopping

This post is adapted from our Definitive Guide to Adopting Agentic Commerce in 2025. Download the full guide for our complete nine-step readiness framework and trust stack implementation strategy.

Fall 2025 will be remembered as the time when agentic commerce first moved from pilot to platform. AI shopping agents are finally gaining the infrastructure needed for true agentic commerce. The payment rails, security frameworks, and merchant integrations needed to handle transactions at scale are becoming a reality.

So far, PayPal has unveiled its agentic commerce services, Mastercard launched Agent Pay with tokenized payments, and OpenAI’s ChatGPT Instant Checkout went live for large retailers like Walmart and Etsy, with over 1 million Shopify merchants to follow.

The data confirms this acceleration is already underway: our recent analysis shows that traffic from AI Agents grew over 1300% in just nine months, with agents primarily focused on product research and discovery.

The question is no longer whether agentic commerce is coming; it’s whether your business is ready when agents arrive at your digital door.

Readiness for this shift requires more than philosophical alignment or high-level strategy. This post breaks down the nine foundational capabilities that define agentic commerce readiness—how to make your catalog machine-readable, monitor and optimize for AI traffic, build agent-ready APIs, secure transactions, govern agent behavior, and embed trust as operational infrastructure. Each section maps to a concrete step in our readiness framework.

If you want the complete blueprint, including phased implementation roadmaps and trust stack architectures—download The Definitive Guide to Adopting Agentic Commerce in 2025.

A 9-Step Blueprint for Agentic Commerce Readiness

Make your catalog machine-readable

AI agents read structured data rather than layouts or marketing copy. If your product information is not structured for machines, your catalog will not appear in agent-driven recommendations.

Begin with schema.org markup in JSON-LD format. Product, Offer, MerchantReturnPolicy, and AggregateRating schemas provide the context agents use to interpret pricing, availability, and customer satisfaction. Structured data also improves retrieval and reduces errors in recommendation systems.

Every product should have consistent, unique identifiers such as SKU, GTIN, and MPN. Agents cross-reference information across sources, and inconsistent identifiers weaken trust in your data.

Maintain clean, standardized catalog feeds. Use consistent attributes, industry taxonomies, and factual descriptions. Specific information such as “100% organic cotton, pre-shrunk, 200 thread count” performs better than vague marketing language like “luxuriously soft bedding.”

Gain visibility into AI traffic signals

Optimization starts with visibility, yet most analytics tools still cannot separate AI agent traffic from human visitors or conventional bots. That blind spot becomes costly as agents evolve from product discovery to autonomous purchasing. Each blocked agent request can mean a lost sale, while each unchecked interaction introduces risk.

Begin with user-agent classification. Many agents identify themselves through useragent, others are cryptographically signed. Analytics and bot management systems should categorize these identifiers to separate legitimate agents, scrapers, malicious bots, and human users. Segmented reporting helps you see which agents browse products, reach checkout, create accounts, or test vulnerabilities.

Since user-agent strings can be spoofed, behavioral baselining provides a stronger layer of insight. Legitimate agents query APIs systematically, follow rate limits, and complete transactions within normal timeframes. Malicious activity shows as bursts of requests, erratic navigation, or repeated failed authentication. HUMAN’s telemetry across 20 trillion weekly interactions shows that about 87 percent of agent traffic targets product pages, with 2.2 percent reaching checkout and 0.1 percent attempting account creation. Establishing these baselines lets you detect anomalies quickly and separate valuable automation from abuse.

Visibility also depends on knowing which agents are currently active. The ecosystem shifts weekly as new platforms appear and existing ones gain transaction features. Monitor for emerging sources such as Perplexity’s Comet browser, ChatGPT Atlas, or agents from Claude with computer-use capabilities. Real-time classification and alerting keep your visibility current as the landscape evolves.

Build agent-ready interfaces for browsing and purchasing

Agents use both APIs and web interfaces, but they depend on structure and predictability rather than design. A commerce experience that works for agents is one that communicates data clearly and behaves consistently.

Treat your product catalog API as a primary interface. It should provide full product information, respond quickly, apply sensible rate limits, and include documentation that machines can parse. REST or GraphQL both work if they deliver complete and stable data.

For agents that browse through web pages, ensure your site can be automated by standard browser frameworks. Keep checkout flows straightforward and predictable. Avoid pop-ups, modals, and unsophisticated CAPTCHAs that interrupt automation. Maintain stable HTML element identifiers and form behavior so agents can complete transactions without failure.

Authentication requires delegated access. OAuth 2.0 flows let users authorize agents with specific, revocable permissions. Frameworks such as PayPal’s Agent Ready and Mastercard’s Agent Pay are creating standards for this process, and early adoption improves compatibility.

Optimize for performance and machine-time latency

Agents operate at machine speed. Latency that humans tolerate causes agents to abandon sessions.

Keep API responses below 200 milliseconds. Use CDNs, caching, and conditional requests to reduce load times and keep catalog data current. Optimize database queries and enable asynchronous processing for traffic surges.

For browser-based agents, prioritize instant data access. Compress assets, remove render-blocking scripts, and place structured data in initial HTML rather than relying on delayed JavaScript rendering.

Set rate limits carefully. Excessive restriction blocks legitimate agents, while permissive limits expose infrastructure to abuse. Use graduated thresholds based on agent classification and adjust as agent traffic grows.

Maintain integrity and consistency

Agents depend on factual accuracy. They cannot rely on visual design or brand cues to resolve discrepancies. A single mismatch between product page, API, or checkout data may be enough for an agent to treat a catalog as unreliable and reduce its ranking.

Keep pricing identical across every channel: product pages, APIs, checkout flows, and third-party listings. Agents compare data across multiple sources, and any discrepancy signals either error or deception. Use a single source of truth for pricing and update caches immediately when changes occur.

Apply the same discipline to inventory. Real-time synchronization across all sales channels prevents agents from promoting out-of-stock products or initiating unfulfillable purchases. Stock changes should propagate to every interface within seconds.

Ensure product attributes, images, and descriptions remain consistent and complete. Frequent edits or missing specifications reduce how clearly agents can represent your products in their embeddings and comparison models. Enforce data governance through required fields, validation checks, and version tracking to preserve accuracy.

Govern and secure transactions

Transaction access defines whether agents can act usefully or dangerously. Allowing them to add items to carts, apply promotions, or process returns requires open flows, but those same interfaces invite abuse if not controlled.

Expose only what you can govern. APIs, browser automation, and embedded checkouts can be probed as easily by attackers as by legitimate agents. Promotion and loyalty endpoints are common targets for scripting, and refund flows can be overwhelmed without safeguards.

Use cryptographic authentication and scoped authorization. Establish agent identity through HTTP signatures, OAuth 2.0, or tokenized credentials. Short-lived tokens for search, payment, or intent declaration limit exposure and keep access auditable.

Apply rate limits and replay protection to block automated coupon testing, scripted returns, and repeated transactions. Segment access by function: read-only endpoints for catalog and availability, separate transactional and administrative interfaces.

Monitor behavior continuously. Track velocity, error rates, and permission violations. Spikes, repeated retries, or unapproved actions should trigger alerts or throttling.

Reassess chargeback and fraud systems. Human-oriented tools that rely on fingerprints, IP reputation, or household data are less effective with agent traffic. Models should shift toward cryptographic identity, agent reputation, and behavior-based risk scoring.

Monitor and manage agent activity

Agent oversight is a continuous process. New agents appear regularly, and behaviors shift as capabilities expand. Maintaining visibility and control is an operational discipline, not a static rule set.

Build and maintain a taxonomy of agents that interact with your systems. Differentiate between commercial shopping agents, enterprise procurement agents, price scrapers, and malicious actors. Each group requires a tailored response: trusted agents get full catalog access, scrapers face rate limits, and attackers are blocked.

Monitor behavior to detect anomalies. Spikes in traffic from a new source may indicate either a launch or an attack. Repeated incomplete checkouts can reveal integration errors or probing activity. Elevated cart abandonment from a specific agent suggests friction in your checkout flow. These indicators warrant investigation and response.

AgentOps applies the principles of DevOps and SecOps to AI-driven commerce. It covers agent onboarding, permission management, incident response, performance monitoring, and enforcement of business rules such as inventory and margin controls.

The goal is not manual intervention but adaptive systems. Trusted agents should operate efficiently, while automated controls detect and stop misuse. When an API change disrupts a legitimate agent, alerts and rollbacks should trigger automatically. When malicious behavior appears, blocking should occur without harming valid traffic.

Secure and enrich first-party data

Agents operate without cookies and rarely accept third-party trackers. They depend on privacy-focused architectures that limit traditional attribution, which makes first-party data collected through direct interactions and authenticated transactions increasingly valuable.

Capture data from agent-mediated purchases: products recommended, items bought, queries made, and which agents users trust. This information shows how agents influence demand and where product fit is strongest.

Authenticated interactions provide the most durable insight. When users authorize agents to access their accounts, merchants gain persistent identity across sessions, purchase history, and explicit preferences such as size, dietary restrictions, or brand affinity. Offer clear value for authentication through loyalty programs or saved preferences to encourage opt-in.

Balance enrichment with privacy. Users delegating purchase authority expect restraint. Explain what data is collected, how it is used, and what control users retain. Use privacy-preserving methods such as aggregation, differential privacy, and limited retention to maintain trust while improving performance.

Agent interactions also generate new fraud indicators. Behavior that diverges from normal user patterns—sudden address changes, unusual purchase velocity, or inconsistent categories—can signal misuse. Incorporate agent reputation, behavioral baselines, and historical relationships into fraud models.

Align on trust as infrastructure

Trust determines whether agentic commerce can scale. It must be embedded in design, governance, and operations from the start.

Trust extends beyond security. It includes reliability, transparency, accountability, and user control. A safe transaction that lacks clarity still damages confidence.

Every function contributes to readiness. Product teams design interfaces that agents can navigate. Engineering maintains consistent performance. Security governs access. Data teams handle enrichment and privacy. Marketing manages discoverability. Finance oversees agent-driven transactions. Legal ensures compliance with delegated authority.

Successful organizations treat trust as shared infrastructure, not a checklist. They create governance models for agent permissions, data sharing, and risk tolerance. They measure satisfaction, accuracy, and fraud outcomes as indicators of trust, not only technical uptime.

Growth follows when agents can operate transparently, users remain protected, and internal teams coordinate around the same standard of reliability and control.

What comes next

The nine readiness areas are not a checklist to finish in sequence but a set of capabilities that mature together. Each reinforces the others as agent traffic grows and your systems adapt.

Agentic commerce is no longer theoretical. It is already operating at scale, with agents driving real transactions across major retail platforms. The merchants gaining advantage are those that have begun aligning data integrity, infrastructure performance, and trust management today rather than waiting for standards to settle.

For a deeper framework on how to build and phase your readiness program, download The Definitive Guide to Adopting Agentic Commerce. It outlines the trust stack model, governance strategy, and technical roadmap for creating agent-compatible commerce systems.