1. What Is Agentic Commerce?

Agentic commerce is the use of AI agents to perform commercial activities on behalf of buyers, sellers, employees, or organizations.

Traditional e-commerce requires the customer to perform most of the work:

Identify a need. Search for products. Open multiple websites. Compare specifications. Read reviews. Investigate sellers. Check inventory. Calculate shipping. Interpret return policies. Apply discounts. Enter payment information. Track the order.

Contact support when something goes wrong. Agentic commerce transfers some or all of this work to software. An agent may receive a broad objective rather than a narrow command. Instead of asking it to open a particular website, the customer may ask it to solve a problem.

For example:

Organize a five-day business trip to Toronto within a fixed budget. Reorder office supplies whenever inventory falls below a certain level. Find a better commercial insurance policy before the existing policy renews. Replace an appliance with the most energy-efficient model that fits the available space. Purchase approved software subscriptions for a new employee. Compare payroll providers for a 50-person company. Monitor the price of a product and purchase it when defined conditions are met. Find a supplier that meets environmental, security, geographic, and delivery requirements. The agent can divide the objective into tasks, collect information, compare options, make recommendations, request approval, and execute approved actions. That ability distinguishes agentic commerce from conventional search. Search engines generally return information and links. Recommendation systems rank possible choices. Chatbots answer questions. Agents can go further by maintaining context, using tools, coordinating steps, interacting with commercial systems, and potentially completing transactions. OpenAI’s Agentic Commerce Protocol, for example, is designed to connect merchants with shoppers using ChatGPT. Its product-feed infrastructure allows participating merchants to provide structured information about products, prices, availability, images, sellers, and promotions so products can be understood and surfaced in relevant shopping experiences.

Google has also introduced commerce infrastructure intended to support agent-assisted shopping, including the Universal Commerce Protocol and tools for retailers to make product information and checkout capabilities accessible across AI-mediated experiences. The direction is becoming clear: AI is moving from helping people think about purchases to helping them perform purchases.

2. Why Agentic Commerce Is More Than Another Sales Channel

It would be easy to treat agentic commerce as another distribution channel beside search engines, social media, marketplaces, mobile applications, and retail stores. That interpretation is too narrow. A new sales channel changes where customers buy. Agentic commerce changes how commercial decisions are formed.

An AI agent can potentially influence:

Whether a customer recognizes a need. Which product category is considered. Which brands qualify for comparison. Which criteria receive the greatest weight. Which claims are trusted. Which compromises are considered acceptable. Which offer provides the best total value. Whether a purchase is completed. Whether the customer remains loyal. Whether the product is returned or replaced. Whether the relationship continues after the transaction. An agent may not behave like a traditional consumer.

It does not become tired after opening ten browser tabs. It does not necessarily prefer the product with the most emotionally attractive advertisement. It can compare thousands of attributes, identify inconsistencies, calculate lifetime cost, examine policies, and remember the customer’s previous experiences. This creates a different competitive environment. In conventional digital marketing, a company may purchase visibility through advertising even when its product information is incomplete or its operational performance is weak.

In agentic commerce, paid visibility may still matter, but an agent may reject the offer if:

The product does not meet the user’s constraints. The price cannot be verified. Inventory data appears unreliable. Delivery is uncertain. The return policy is ambiguous. Reviews reveal recurring problems. The warranty is weaker than competing offers. The seller cannot be authenticated. Important product attributes are missing. The total cost is higher after fees and maintenance. The transaction requires excessive friction. The merchant cannot safely accept agent-initiated purchases.

A brand can therefore be highly visible to humans while remaining unattractive or invisible to machines.

3. Your Business Now Has Two Audiences

For decades, marketing departments concentrated primarily on human buyers. Companies studied demographics, emotions, habits, aspirations, social identities, purchasing triggers, and customer journeys. They developed brand stories, advertising campaigns, packaging, retail experiences, content, loyalty programs, and sales processes around human psychology. Those capabilities remain important. People will continue to care about beauty, identity, status, meaning, trust, convenience, belonging, and emotional connection. However, the buying process may increasingly involve a second audience: the customer’s agent. The human and the agent may evaluate the same offer differently.

The human may ask:

Does this brand understand me? Do I identify with its values? Is this product attractive? Does it feel premium? Will it improve my life? Do I trust the company? Does owning it say something about me?

The agent may ask:

Does the product satisfy the stated requirements? Are the specifications complete? Is the price current? Is the item available? What is the total cost of ownership? Can the claim be verified? What warranty applies in the customer’s location? How quickly can the product arrive? How often is it returned? Are there credible alternatives? Does the merchant meet the customer’s policies? Can the transaction be completed securely?

What happens when an exception occurs? Successful brands must answer both sets of questions. This means brand strategy and data strategy can no longer remain separate. A compelling brand promise must be supported by product data, operational performance, evidence, policies, service records, reviews, certifications, and reliable commercial infrastructure. The machine-readable version of the brand must be as carefully managed as the visual version.

4. The New Customer Journey

The traditional marketing funnel is often described as awareness, consideration, conversion, retention, and advocacy. Agentic commerce may compress, rearrange, or partially conceal these stages. Stage 1: The customer expresses an intention The journey may begin with an outcome rather than a product name.

Examples include:

Help me sleep better. Reduce my company’s cloud costs. Find a safer family vehicle. Replace our accounting software. Buy healthy groceries for the week. Plan a sustainable vacation. Find a gift for someone who enjoys architecture. Keep our warehouse stocked without overordering. The agent converts this intention into criteria. Stage 2: The agent builds a consideration set The agent identifies possible products, services, sellers, and combinations. A brand that does not provide understandable information may never enter this set.

This is the first major danger of agentic commerce: invisibility can occur before the customer knows that the brand was considered. Stage 3: The agent evaluates evidence

The agent may examine:

Product specifications. Prices and promotions. Inventory. Seller reputation. Reviews. Delivery performance. Return rates. Warranties. Compatibility. Certifications. Safety information. Sustainability claims.

Regulatory status. Historical customer experiences. Total cost of ownership. Stage 4: The agent resolves trade-offs Few products are objectively best in every category. The agent may decide that one product is more expensive but offers better durability, another is cheaper but has a restrictive return policy, and a third offers faster delivery but weaker support. The final recommendation depends on the customer’s priorities. Stage 5: The agent recommends or acts

Depending on its authorization, the agent may:

Present a shortlist. Ask the customer to approve one option. Negotiate or request an offer. Reserve inventory. Complete checkout. Use a delegated payment credential. Schedule delivery. Record the purchase. Update a budget or accounting system. Stage 6: The agent manages the relationship

After purchase, the agent may:

Track delivery. Request installation. Activate the warranty. Monitor product performance. Reorder consumables. Cancel unused subscriptions. File a return. Request a refund. Compare renewal options. Recommend switching to a competitor. The commercial relationship may therefore continue through the customer’s agent. This turns post-purchase service into an important source of machine-evaluated reputation.

5. Brand Loyalty Will Become More Conditional

Traditional loyalty often depends on familiarity, habit, convenience, identity, accumulated rewards, or reluctance to search for alternatives. AI agents reduce the cost of comparison. A customer who previously purchased the same brand repeatedly because researching alternatives was inconvenient can now ask an agent to reassess the market before every transaction. Accenture reports that 90 percent of frequent AI users in its North American research said they would be open to switching from a preferred brand when an AI assistant presented a better alternative. That does not mean brand loyalty will disappear. It means loyalty will need stronger foundations.

Brands can maintain preference through:

Superior product performance. Reliable fulfillment. Transparent pricing. Strong warranties. Excellent support. Personalized services. Integrated ecosystems. Membership benefits. Ethical trust. Proprietary products. Customer data used responsibly. Demonstrable long-term value.

Compatibility with the customer’s existing environment. Reduced risk and switching costs. Consistently positive outcomes. Weak loyalty built mainly on customer inertia is vulnerable. Strong loyalty built on verified value may become even more powerful because agents can repeatedly confirm that the brand remains the right choice.

6. What Makes a Brand Visible to AI Agents?

Human-facing visibility has traditionally depended on factors such as advertising reach, store locations, packaging, search ranking, social content, reviews, public relations, and marketplace placement. Agent visibility requires additional capabilities.

6.1 Structured product and service data

Agents need reliable data fields rather than vague promotional descriptions.

For a physical product, these may include:

Product name. Brand. Model. Category. Description. Technical specifications. Dimensions. Weight. Materials. Variants. Compatibility. Price.

Currency. Availability. Seller identity. Images. Warranty. Return policy. Delivery options. Certifications. Country restrictions. Maintenance requirements. Total ownership costs.

For a service, the information may include:

Scope. Deliverables. Eligibility. Geographic coverage. Pricing model. Contract period. Service-level commitments. Required integrations. Cancellation terms. Support availability. Compliance credentials. Implementation time.

Limitations and exclusions. OpenAI’s commerce documentation emphasizes the importance of complete, current, variant-specific product information, including accurate titles, descriptions, pricing, availability, media, seller details, and policy links.

6.2 Semantic clarity

Machine-readable does not simply mean placing data in a database. The information must be clear enough for an agent to interpret meaningfully. A phrase such as “industry-leading durability” provides little comparative value without evidence.

A stronger description might explain:

Tested operating temperature. Expected service life. Warranty duration. Repairability score. Drop-resistance standard. Replacement-part availability. Measured failure rate. Independent certification. The goal is to transform promotional claims into decision-ready facts.

6.3 Freshness

Outdated information destroys trust.

An agent may exclude a brand after encountering:

Expired prices. Incorrect inventory. Discontinued products. Broken product links. Contradictory warranty terms. Promotions that cannot be redeemed. Delivery estimates that are routinely missed. Product descriptions that do not match the delivered item. Catalog synchronization, inventory accuracy, pricing governance, and policy consistency become core marketing capabilities.

6.4 Accessibility

Agents must be able to retrieve information through supported technical mechanisms.

These may include:

Product feeds. Merchant APIs. Structured web data. Commerce protocols. Marketplace integrations. Search indexes. Authorized agent interfaces. Real-time inventory systems. Checkout APIs. Customer-service interfaces. OpenAI’s Agentic Commerce Protocol and Google’s Universal Commerce Protocol illustrate the emergence of standardized connections between merchants, AI platforms, and transaction systems.

6.5 Consistency

Agents can compare information across multiple sources. If a company describes the same product differently on its website, marketplace listings, distributor portals, support pages, and product feeds, those inconsistencies can reduce confidence.

A brand therefore needs a trusted source of truth for:

Product identity. Specifications. Pricing. availability. policies. claims. certifications. promotional eligibility. geographic restrictions.

7. From SEO to Agent Optimization

Search engine optimization helps content become discoverable and understandable to search engines. Agent optimization extends this idea into decision-making and transaction execution. The objective is not merely to rank for a keyword.

The objective is to become:

Discoverable for a customer’s intention. Eligible under the customer’s constraints. Understandable during comparison. Credible during verification. Competitive during recommendation. Transactable during execution. Reliable after the purchase. This requires several overlapping disciplines. Search Engine Optimization SEO helps a brand appear in search results. Generative Engine Optimization Generative engine optimization helps a brand and its information appear accurately in AI-generated answers.

Recommendation Engine Optimization Recommendation optimization improves the likelihood that products are included in personalized recommendation systems. Marketplace Optimization Marketplace optimization improves visibility, conversion, ratings, and fulfillment performance within platforms such as Amazon, Walmart, or specialized industry marketplaces. Agent Commerce Optimization Agent commerce optimization makes a business understandable and usable by AI systems that may evaluate and act on behalf of buyers.

Agent commerce optimization may involve:

Product feed quality. Entity consistency. Policy clarity. API availability. Real-time inventory. Machine-readable offers. Evidence-backed claims. Secure agent identification. Delegated checkout. Reliable fulfillment. Transparent post-purchase processes. The brands that treat these disciplines as unrelated projects may create fragmented experiences. The stronger approach is to build a unified commercial knowledge layer that supports all discovery and transaction environments.

8. Why Traditional Advertising Will Not Be Enough

Advertising is designed primarily to influence human attention and perception. Agentic commerce introduces a decision-maker that may be less responsive to many conventional persuasion techniques.

An agent may not care that:

A celebrity endorses the product. The advertisement uses emotional music. The packaging appears luxurious. The brand purchased the largest banner. The company repeats the word “premium.” A countdown timer creates artificial urgency. The seller hides fees until checkout. The offer is described as exclusive without proof. This does not make branding irrelevant. Emotional meaning will continue to influence human preferences. A customer can instruct an agent to prefer a particular aesthetic, status level, country of origin, environmental policy, or brand identity. However, the agent may test whether the product actually meets the customer’s stated needs. Advertising must therefore become more tightly connected to evidence.

A powerful future brand will combine:

Emotional distinctiveness for people. Structured clarity for machines. Operational reliability for transactions. Verifiable evidence for trust. A brand story may create desire. Machine-readable evidence may determine whether the brand qualifies.

9. The Importance of Machine-Readable Value

Companies often communicate value through broad language:

Better. Smarter. Premium. Sustainable. Trusted. Innovative. High performance. Customer-centric. Best in class. These phrases are difficult to evaluate without context.

An AI agent may need to know:

Better than what? Smarter in which measurable way? Premium because of which materials or services? Sustainable under which standard? Trusted by whom? Innovative compared with which baseline? High performance under which conditions? Best in class according to which independent assessment? Businesses should create a value evidence system.

For each important claim, record:

The exact claim. The applicable product or service. The supporting evidence. The methodology behind the evidence. The source. The date. The geographic applicability. The limitations. The expiration or review date. The approved channels in which the claim may appear. This helps marketing, legal, product, sales, and AI systems communicate consistently. It also reduces the risk that agents encounter unsupported or contradictory claims.

10. The Two Strategic Positions: Choice of Agents or Agent of Choice

Accenture identifies two broad strategic positions. Position One: Become a Choice of Agents Nearly every company needs to pursue this position. The objective is to make the company’s products and services easy for external agents to find, understand, compare, recommend, and purchase.

This requires:

Structured product information. Real-time commercial data. Competitive offers. Accessible policies. Reliable transactions. Clear brand identity. Strong reputation. Secure agent interactions. Excellent fulfillment. Low exception rates. The company remains a supplier or merchant, while other organizations control the agent relationship. Position Two: Become an Agent of Choice

Some category leaders may build their own trusted agent. This agent could help customers solve problems across a category rather than merely sell one company’s products.

Examples could include:

A beauty agent that recommends routines and products. A travel agent that plans, books, and manages trips. A healthcare-navigation agent. A financial-planning agent. A home-maintenance agent. A procurement agent for small businesses. A construction-material sourcing agent. A vehicle ownership and maintenance agent. A nutrition and grocery agent. Accenture highlights Noli, a beauty technology venture backed by L’Oréal and powered by Accenture, as an example of category expertise, consumer data, and AI being combined into a specialized recommendation experience. Accenture reports that 98 percent of surveyed Noli users described their match positively and 90 percent said it improved shopping confidence. Becoming an agent of choice is significantly more demanding.

The company needs:

Deep domain expertise. Trusted first-party data. Strong customer relationships. High-quality recommendations. Broad product or partner access. Transparent commercial incentives. Effective governance. Secure identity and payment infrastructure. Permission to act. A reason for customers to delegate decisions. A poorly designed brand-owned agent that recommends only the company’s own products may be perceived as an automated salesperson. A trusted agent must provide meaningful customer value, explain trade-offs, protect the user’s interests, and disclose commercial relationships.

11. Payments Become a Strategic Brand Capability

Agentic commerce cannot scale without reliable authorization and payment systems. The central question is not simply whether an agent can technically submit a payment.

The system must determine:

Which agent is acting? On whose behalf is it acting? What is the agent permitted to purchase? What spending limit applies? Did the customer approve this transaction? Is the merchant legitimate? Is the offer still valid? Can the agent use the payment credential? Who is responsible if the transaction is disputed? How should refunds be processed? Can the customer revoke the agent’s authority? How can malicious bots be separated from authorized agents?

Visa’s Trusted Agent Protocol is intended to help merchants distinguish recognized commerce agents from malicious automated traffic using verifiable credentials and cryptographic signals. Visa describes capabilities for communicating agent identity, intent, and aspects of consumer recognition across existing commercial infrastructure. Mastercard’s Agent Pay initiative similarly focuses on agent identity, tokenized credentials, permissioning, transaction controls, and secure payment execution. In 2026, Mastercard also announced infrastructure for machine-driven payments across cards, accounts, and stablecoins, with credentialing, spending rules, and settlement controls. For brands, payment readiness affects more than checkout convenience.

An agent may prefer merchants that provide:

Transparent total pricing. Immediate confirmation. Reliable authorization. Strong fraud controls. Flexible payment options. Machine-readable promotions. Fast refunds. Clear dispute procedures. Secure delegated credentials. Predictable settlement. A confusing or unreliable payment process can cause a brand to lose the transaction even after winning the recommendation.

12. Agentic Commerce Changes the Economics of Digital Selling

Digital commerce contains many hidden costs:

Customer acquisition. Repeated advertising. Cart abandonment. Customer support. Fraud. Returns. Restocking. Payment disputes. Failed deliveries. Incorrect orders. Price comparison. Excessive promotions.

Low-quality leads. Inventory imbalance. Accenture argues that well-designed agentic commerce can reduce some of these costs by helping customers make clearer decisions, improving product matching, reducing confusion, and automating routine interactions. It also emphasizes the importance of inventory accuracy, payment performance, supply-chain responsiveness, and low-error operating models. Consider returns.

A customer may return a product because:

The size was wrong. The product was incompatible. An important feature was missing. The image was misleading. Delivery occurred too late. The customer misunderstood the policy. The product did not fit the intended use. An agent with access to accurate product information and customer requirements may prevent some of these mistakes. However, automation can also create new costs.

Poorly governed agents may:

Place duplicate orders. Misinterpret preferences. Exploit pricing errors. Trigger excessive returns. purchase unauthorized products. interact with fraudulent sellers. overwhelm support systems. generate high volumes of low-value transactions. The economic benefit depends on governance, data quality, authorization, system design, and exception management.

13. Supply Chains Must Respond at Machine Speed

An AI agent can compare options and initiate transactions much faster than a human customer. This creates pressure on supply chains. A merchant cannot promise immediate availability when its inventory systems update once per day.

Agent-ready supply chains need:

Real-time inventory visibility. Accurate location data. Reliable delivery estimates. Automated reservation. Dynamic fulfillment selection. Substitution rules. Exception handling. Supplier coordination. Product traceability. Return routing. Capacity awareness. Accenture argues that agent-mediated demand will require more responsive forecasting and flexible fulfillment models, including ship-from-store, scheduled delivery, pickup, micro-fulfillment, and other inventory-placement strategies.

The agent may consider fulfillment quality as part of the product itself. A slightly more expensive product that can arrive reliably tomorrow may be preferable to a cheaper alternative with uncertain delivery. Operational data therefore becomes part of the brand promise.

14. Trust Will Become a Competitive Advantage

Agentic commerce introduces new trust questions for customers, merchants, financial institutions, and platforms.

Customers must trust:

The agent. The agent provider. The recommendation. The data used. The merchant. The payment process. The handling of personal information. The agent’s interpretation of their intentions.

Merchants must trust:

The identity of the agent. The authority given by the customer. The validity of the payment. The customer information presented. The agent’s compliance with terms. The legitimacy of automated traffic.

Platforms must trust:

Merchants. Product feeds. sellers. transaction partners. identity systems. payment credentials. agent developers.

Brands can strengthen trust through:

Transparent ownership. Verified merchant identity. Accurate product information. Clear pricing. Authentic reviews. Independent certifications. Published policies. Strong security. Reliable service records. Explainable recommendations. Disclosure of sponsorship and commissions. Responsible use of customer data.

Rapid correction of errors. In the agentic economy, trust may become machine-verifiable. A company’s reputation will not exist only in brand awareness studies. It may be reflected in structured credentials, transaction history, dispute rates, delivery performance, policy compliance, security records, and customer outcomes.

15. The Agentic Commerce Control Tower

Agentic commerce cannot be managed by the marketing department alone.

It affects:

Marketing. Product. E-commerce. Sales. Customer service. Information technology. Data. Artificial intelligence. Cybersecurity. Payments. Finance. Supply chain.

Legal. Privacy. Risk. Compliance. Accenture recommends a cross-functional control-tower approach to coordinate these areas and monitor how agents interact with the business. A practical agentic commerce control tower could oversee five layers. Layer 1: Discovery

Monitor:

Whether agents can find the brand. Which products are surfaced. Which customer intentions trigger inclusion. Whether descriptions are accurate. How the brand compares with competitors. Layer 2: Decision

Monitor:

Recommendation frequency. Reasons for selection. Reasons for rejection. Policy conflicts. Missing attributes. Price competitiveness. Evidence quality. Layer 3: Transaction

Monitor:

Agent authentication. Authorization success. Payment completion. fraud signals. checkout errors. promotion eligibility. tax and delivery calculations. Layer 4: Fulfillment

Monitor:

Inventory accuracy. delivery performance. substitutions. cancellations. exception rates. return rates. refund speed. Layer 5: Governance

Monitor:

Privacy. security. regulatory compliance. customer consent. model behavior. bias. commercial disclosures. audit records. incident response. The control tower should not become another dashboard that nobody uses. It needs clear owners, escalation procedures, service levels, and authority to correct problems across departments.

16. New Metrics for the Agentic Commerce Era

Traditional commerce metrics remain useful, but they will not tell the entire story. Brands may need a new measurement system. Agent Discoverability Rate How often does the brand appear when agents research needs relevant to its products? Qualified Inclusion Rate How often does the brand enter the consideration set after customer constraints are applied? Agent Recommendation Share Among relevant recommendations, how often is the brand recommended? Machine Selection Rate How often do agents select the brand when authorized to make a decision? Agent Bypass Rate How often is the brand found but excluded?

Attribute Completeness What percentage of important product or service fields are populated accurately? Data Freshness How quickly are changes in price, availability, policy, and product information reflected across agent-accessible systems? Claim Verifiability What percentage of important marketing claims have accessible supporting evidence? Transaction Completion Rate How many agent-initiated checkouts are completed successfully? Exceptions per Thousand Transactions How many purchases require human intervention because of data, payment, inventory, policy, or fulfillment problems? Agent-Mediated Return Rate Are products selected by agents returned more or less often than conventionally selected products?

Cost to Serve Does agent-assisted commerce reduce support, acquisition, fulfillment, and return costs? Customer Override Rate How often does the human customer reject the agent’s recommendation? Relationship Retention Does the customer or agent return to the brand for replenishment, renewal, support, or related purchases? These metrics help reveal why a business is winning or losing inside AI-mediated journeys.

17. A Practical Agentic Commerce Readiness Framework

Companies do not need to transform everything at once. They can proceed in stages. Stage One: Establish Visibility Create a complete map of where product, service, brand, and policy information currently exists.

Identify:

Missing attributes. Contradictory descriptions. Outdated content. Unstructured policies. Broken integrations. Inaccurate inventory. Unsupported claims. Inconsistent seller identities. The goal is to make the brand legible. Stage Two: Build a Trusted Commercial Data Layer

Create governed sources of truth for:

Product information. Customer-facing policies. prices. promotions. inventory. sellers. evidence. certifications. fulfillment. warranties. Assign ownership to each data domain. The goal is to make the brand reliable.

Stage Three: Connect to Agent Ecosystems

Evaluate integrations with:

AI shopping platforms. Product-feed systems. commerce protocols. marketplaces. payment networks. identity services. customer-service agents. fulfillment partners. The goal is to make the brand accessible. Stage Four: Enable Safe Transactions

Implement:

Agent recognition. authorization. consent. spending controls. delegated payment. fraud detection. audit logs. refund handling. dispute management. human approval points. The goal is to make the brand transactable. Stage Five: Optimize Selection

Analyze:

Why agents recommend competitors. Which attributes influence decisions. Which policies create friction. Which claims lack evidence. Which combinations of price, delivery, warranty, and service improve selection. The goal is to make the brand competitive. Stage Six: Build an Agent-Native Customer Relationship

Allow customers and authorized agents to:

Manage accounts. Retrieve records. reorder products. renew services. change subscriptions. submit claims. schedule support. manage returns. update preferences. The goal is to make the relationship persistent. Stage Seven: Consider Building a Specialized Agent Only after establishing trust, data, governance, and domain authority should a company consider becoming an agent of choice.

The goal is to become a valued intermediary, not merely another seller with a chatbot.

18. A 90-Day Action Plan

Days 1 - 30: Diagnose Appoint an executive sponsor. Form a cross-functional working group. Select one product category or customer journey. Audit product and service data. Audit warranties, returns, delivery, and pricing information. Test how major AI assistants currently describe the brand. Identify inaccurate or missing information. Compare machine-readable data with leading competitors. Map payment, identity, inventory, and checkout limitations. Define initial metrics. Days 31 - 60: Repair

Correct inconsistent product records. Standardize terminology. improve descriptions and attributes. publish clear policy information. Connect claims with evidence. improve inventory and pricing synchronization. define agent-access rules. strengthen merchant identity and security controls. create an exception-management process. prepare a controlled pilot. Days 61 - 90: Pilot Launch one product feed or agent-facing integration.

Test discovery across realistic customer intentions. measure inclusion and recommendation. test delegated checkout in a controlled environment. simulate fraud, cancellation, refund, and return cases. monitor human overrides. collect customer feedback. correct operational failures. calculate economic impact. decide whether to expand, redesign, or pause. A limited pilot is more valuable than a large announcement unsupported by working infrastructure.

19. Risks Businesses Must Address

Loss of Customer Ownership When an external agent controls discovery and interaction, the brand may lose direct access to the customer. Companies need permission-based mechanisms for maintaining service relationships without undermining customer choice. Algorithmic Dependency A brand may become dependent on a small number of agent platforms. Businesses should avoid building their entire strategy around one proprietary ecosystem. Manipulation and Pay-to-Play Agent recommendations could become distorted by undisclosed sponsorships, commissions, or platform incentives. Transparent commercial disclosure will be essential. Data Exposure Agents may request sensitive customer, inventory, contract, or pricing information. Access must be limited according to identity, purpose, permission, and context.

Fraudulent Agents Malicious bots may impersonate authorized shopping agents. Merchant systems need verification, rate controls, authentication, and risk monitoring. Incorrect Purchases Agents can misunderstand ambiguous instructions or act on incomplete data. Higher-risk purchases should require stronger confirmation and human approval. Discrimination and Bias Agents may reproduce bias in product selection, pricing, credit, insurance, employment-related procurement, or service access. Companies should test outcomes and maintain review procedures. Price Compression Agents can make comparison easier and increase pressure on margins. Brands must differentiate through performance, service, trust, ecosystem value, and lower lifetime cost rather than relying only on information asymmetry.

Brand Commoditization When products are compared mainly through structured attributes, distinctive brands may appear interchangeable. Human meaning and machine-verifiable value must be developed together. Operational Overload Agents can initiate transactions around the clock and at high volume. Rate limits, spending controls, inventory protection, and exception handling are essential.

20. Opportunities for Startups

Agentic commerce will create infrastructure needs across the commercial ecosystem.

Potential startup categories include:

Agent Visibility Platforms Tools that measure how frequently brands and products appear in AI-generated recommendations. Product Data Optimization Services that clean, structure, enrich, and synchronize product information for agent ecosystems. Agent Reputation Systems Infrastructure for evaluating the reliability of agents, merchants, sellers, and commercial services. Agent Identity and Authentication Systems that verify which agent is acting, who authorized it, and what permissions it has. Delegated Payment Infrastructure Programmable accounts, wallets, cards, spending limits, approval workflows, and transaction credentials for AI agents. Agentic Commerce Analytics Dashboards that explain why agents selected or rejected particular products.

Machine-Readable Policy Infrastructure Tools that convert warranties, returns, shipping, eligibility, and contract terms into structured decision data. Offer and Promotion Engines Systems that publish discounts, bundles, eligibility rules, and loyalty rewards in forms that agents can interpret correctly. Agent Fraud Prevention Security systems that distinguish authorized commerce agents from scraping bots, malicious automation, and fraudulent identities. Agent-Ready Customer Service APIs and services that allow customer agents to resolve routine issues without waiting for human support. Agentic Procurement Specialized agents for supplier sourcing, contract comparison, replenishment, expense control, and business purchasing. Brand-Owned Vertical Agents Category specialists for travel, beauty, finance, real estate, healthcare navigation, automotive ownership, education, professional services, or industrial procurement.

Agent Commerce Compliance Governance, audit, consent, privacy, and regulatory tools for agent-mediated transactions. These opportunities extend far beyond creating shopping chatbots. The largest value may emerge in the invisible infrastructure that makes agent-driven decisions trustworthy, explainable, and executable.

Key Takeaways

Agentic commerce transfers parts of product discovery, comparison, decision-making, purchasing, and post-purchase management to AI agents. Every business may soon have two audiences: the human customer and the agent representing that customer. A brand can be well known among people but invisible to AI agents if its commercial information is incomplete, inaccessible, or unreliable. Product data is becoming part of brand strategy. Advertising cannot compensate indefinitely for weak product information, unclear policies, unreliable inventory, or poor fulfillment. Brand loyalty will not disappear, but agents will make it easier for customers to reconsider alternatives. Strong loyalty will depend on verified value, performance, service, trust, and customer outcomes. Businesses should aim first to become a reliable choice of agents. Category leaders may pursue the more ambitious position of becoming an agent of choice. Payments, identity, permissioning, fraud prevention, and merchant verification are foundational components of agentic commerce. Agent-ready commerce requires cooperation across marketing, product, IT, data, AI, security, payments, supply chain, customer service, legal, and finance. New metrics are needed to measure agent discoverability, recommendation, exclusion, transaction success, exceptions, and retention.

A controlled pilot should begin with one category, one journey, and clearly defined success measures. Brands should not attempt to manipulate agents. They should make their genuine value easier to understand and verify. The long-term winners will combine emotional meaning for humans, structured clarity for machines, operational excellence for transactions, and trustworthy governance for everyone.

Frequently Asked Questions

What is agentic commerce?

Agentic commerce is a commercial model in which AI agents perform activities such as researching products, comparing offers, recommending options, completing purchases, managing delivery, and handling post-purchase tasks for customers or organizations.

Is agentic commerce the same as conversational commerce?

No. Conversational commerce allows customers to interact with businesses through chat or voice interfaces. Agentic commerce can include conversation, but the agent may also plan, compare, use tools, interact with multiple systems, and take approved actions.

Will AI agents replace online stores?

Not entirely. Online stores will remain important for discovery, brand experience, detailed research, account management, customer service, and direct relationships. However, some customers may complete more of their buying journey through AI interfaces without navigating a conventional store.

Will customers still care about brands?

Yes. Customers will continue to care about trust, identity, quality, status, aesthetics, ethics, familiarity, and emotional meaning. However, agents may test whether a brand’s products actually satisfy the customer’s requirements.

How can a brand become visible to AI agents?

The company should provide complete and current product information through structured feeds, APIs, standardized commerce protocols, accessible web content, reliable marketplace listings, and consistent policy documentation.

What is the most important first step?

Audit the company’s product, pricing, inventory, policy, warranty, delivery, and seller data. A business cannot become agent-ready when its own systems disagree about what it sells and under which conditions.

Does agent optimization replace SEO?

No. SEO, generative engine optimization, marketplace optimization, and agent optimization overlap but serve different parts of the journey. Companies will likely need all of them.

Commercial placements and sponsored recommendations will probably exist. However, platforms will need transparent disclosure, and paid visibility may not guarantee selection when the product fails to meet the customer’s requirements.

What information should be machine-readable?

Important information includes product attributes, variants, prices, inventory, delivery, sellers, warranties, return policies, certifications, promotions, geographic restrictions, compatibility, and service terms.

How will agents make payments?

Approaches may include tokenized credentials, delegated cards, digital wallets, account-to-account payments, programmable spending limits, approval rules, and specialized agent-payment protocols.

How can merchants distinguish good agents from malicious bots?

Emerging frameworks use identity credentials, cryptographic verification, permission signals, transaction intent, and trusted commercial networks to help merchants recognize authorized agents.

What happens when an agent makes a mistake?

Responsibility will depend on the platform, merchant agreement, payment mechanism, user authorization, and applicable law. Businesses should maintain audit logs, confirmation rules, reversal procedures, human escalation, and clear liability terms.

Will agentic commerce reduce returns?

It may reduce returns when agents improve product matching and clarify compatibility, sizing, delivery, and policies. Poor data or poorly designed agents could produce the opposite result.

Which industries will adopt it first?

Adoption is likely to be strongest where digital data, comparison, payment, and fulfillment are already mature. Examples include retail, travel, software, financial services, subscriptions, procurement, media, consumer electronics, and business services.

Should every brand build its own agent?

No. Most brands should initially focus on becoming understandable and transactable through external agents. Building a trusted customer-facing agent requires domain authority, strong data, meaningful utility, governance, and sustained investment.

How can smaller businesses compete?

Smaller businesses can compete through accurate data, specialization, superior service, transparent policies, credible expertise, differentiated products, local availability, and fast fulfillment. Agentic commerce may reward genuine relevance even when the company has a smaller advertising budget.

Is agentic commerce already happening?

Yes, although capabilities and adoption remain uneven. Major AI, commerce, and payment companies are developing product feeds, commerce protocols, agent checkout systems, merchant tools, identity frameworks, and delegated payment infrastructure.

Conclusion

Agentic commerce is not simply a faster version of online shopping. It is the beginning of a commercial environment in which software can represent customer intentions, evaluate alternatives, interact with merchants, execute transactions, and manage ongoing relationships. This changes the nature of visibility. A brand is no longer visible merely because customers recognize its logo or because its advertisement appears at the top of a page. The brand must be visible within the systems that agents use to understand the market.

It must clearly communicate:

What it offers. Who it serves. What the product does. What the product costs. Whether it is available. Why its claims should be trusted. How quickly it can deliver. What protections the customer receives. Whether the transaction can be completed safely. What happens after the purchase. The future will not necessarily belong to the company with the loudest advertising. It may belong to the company that is easiest to understand, safest to trust, simplest to transact with, and most reliable at delivering the promised outcome.

Brands must therefore become bilingual. They must communicate meaning, identity, and emotion to people. They must communicate structure, evidence, eligibility, and reliability to machines. A company that succeeds with only one audience will remain vulnerable. A company that earns the confidence of both can become not only visible, but unmissable.

Relevant Articles and Resources

1. Accenture: Agentic Commerce, Make Your Brand Unmissable

The foundational research behind this article. It explains why brands now serve both people and agents, outlines the distinction between becoming a choice of agents and an agent of choice, and examines the effects on payments, supply chains, operations, talent, and commercial economics.

2. OpenAI: Agentic Commerce Protocol

OpenAI’s developer documentation describes how merchants can provide structured product information and support AI-mediated product discovery and commerce.

3. OpenAI: Product Feed Specifications and Best Practices

Practical technical guidance for organizing product variants, pricing, availability, seller information, media, policies, and promotional data for AI shopping experiences.

4. Google: Universal Commerce Protocol and Agentic Retail Tools

Google’s commerce resources explain its work on open standards, AI shopping tools, merchant data, brand agents, universal carts, and agent-enabled checkout experiences.

5. Visa: Trusted Agent Protocol

Visa’s technical and commercial materials explain how merchants may verify recognized agents, interpret agent intent, and distinguish authorized commercial automation from malicious bots.

6. Mastercard: Agent Pay

Mastercard’s Agent Pay materials discuss agent credentials, tokenized payments, authorization, permissioning, and trusted execution for AI-mediated transactions.

7. Mastercard: Agent Pay for Machines

A useful resource for understanding how payment infrastructure may extend from consumer shopping agents into continuous machine-to-machine and business transactions across multiple payment rails.

8. OpenAI: Product Discovery in ChatGPT

An overview of AI-assisted shopping experiences, product comparison, merchant integration, and the developing role of the Agentic Commerce Protocol in product discovery.

9. OpenAI: Instant Checkout and Agentic Commerce

Background on the development of commerce capabilities that connect AI interfaces, merchants, customers, and payment systems during checkout.

10. Visa: Agentic Commerce and the Expanded Payments Economy

A broader explanation of how identity, authorization, payment infrastructure, and customer preferences may work together in an agent-mediated economy.