Introduction: The Third Wave of Commerce
Commerce stands at the threshold of its most significant transformation since the emergence of e-commerce itself. Just as e-commerce fundamentally disrupted brick-and-mortar retail in the early 2000s, agentic commerce — where autonomous AI agents act as intelligent intermediaries between consumers and merchants — promises to reshape every aspect of how products are discovered, evaluated, and purchased. This isn’t merely an incremental evolution in shopping technology; it represents a fundamental restructuring of the entire commercial value chain, with profound implications for brands, retailers, and consumers alike.
Scott Friend has characterized agentic commerce as the “third wave” of commerce, following traditional retail and e-commerce. While e-commerce brought shopping online and enabled global reach, it largely replicated traditional retail models in digital form — consumers still manually browsed catalogs, compared options, and completed purchases. Agentic commerce breaks from this model entirely. Instead of consumers actively shopping, AI agents operating on their behalf make proactive, context-aware decisions aligned with user preferences, goals, and constraints.
The shift is already underway. By late 2024, Perplexity launched “Buy with Pro,” enabling AI-powered shopping directly through search results. OpenAI introduced Operator in January 2025, using agents to automate tasks like booking travel and reservations, then integrated agentic shopping capabilities directly into ChatGPT through the Agentic Commerce Protocol developed with Stripe. More than half of consumers anticipate using AI assistants for shopping by end of 2025, according to Adobe research, while traffic to US retail sites from GenAI browsers and chat services increased 4,700% year-over-year in July 2025.
This article examines the implications of this transformation for all stakeholders. We explore how AI agents function as autonomous shoppers, analyze the disintermediation risks facing brands and retailers, investigate zero-click commerce trends, and provide strategic guidance for organizations navigating this seismic shift.
Understanding AI Shopping Agents
From Passive Tools to Autonomous Actors
AI shopping agents represent a fundamental departure from previous consumer technologies. Unlike traditional e-commerce tools that simply enable human shopping, these agents act independently on users’ behalf. The distinction is crucial: these systems don’t just recommend products or facilitate transactions — they search, evaluate, decide, and purchase with minimal or even no human intervention.
Unlike traditional AI systems that simply respond to commands, agentic AI agents can plan, set goals, adapt to their environment, and act autonomously with minimal human input. In shopping contexts, this means an agent could interpret a request like “Book me a nonstop flight to London for under $600 next week — no red-eyes,” then review airlines, nearby airports, loyalty memberships, and payment card rewards to identify the best option, purchase it, and communicate confirmation — all without requiring additional human input beyond the initial instruction.
Research distinguishes between two primary types of agents in the commerce ecosystem. Buyer agents represent consumers, interpreting natural language intent and taking autonomous actions based on prompts. They leverage large language models (LLMs) and are designed using prompt engineering or fine-tuning techniques to execute digital commerce tasks. Seller agents, conversely, operate on behalf of merchants to optimize pricing, promotions, procurement, and inventory, while directing responses to buyer agents.
This dual-sided agent ecosystem creates a fundamentally different market dynamic than traditional e-commerce, where human consumers interact with merchant websites and applications. Instead, we’re moving toward agent-to-agent transactions where buyer and seller agents negotiate, compare, and transact largely independently of direct human involvement.
Technical Architecture and Capabilities
Modern shopping agents leverage sophisticated technical architectures combining multiple AI capabilities. At their foundation, large language models provide natural language understanding enabling agents to interpret user requests, understand product descriptions, and communicate with merchants. Machine learning models analyze behavioral data, purchase patterns, and contextual signals to make predictions about user preferences and likely satisfaction.
The architecture typically includes several key components. Identity and authentication systems establish secure agent credentials and time-limited identity tokens defining access scope. This ensures agents can act on behalf of users while maintaining appropriate security boundaries. Memory systems maintain both short-term context for immediate tasks and long-term user preferences, purchase history, and behavior patterns enabling personalization.
Tool integration enables agents to access external systems including merchant APIs, price comparison engines, inventory databases, and payment processing systems. Retrieval-augmented generation (RAG) architectures help agents access current product information, reviews, and specifications while maintaining accuracy. Decision-making engines combining rule-based logic with learned preferences determine which products meet user needs and represent good value.
Current implementations vary in sophistication. Simpler agents function primarily as enhanced search and recommendation systems, helping users discover relevant products but requiring human approval for purchases. More advanced agents can complete entire purchase workflows autonomously, from initial search through payment processing, only involving humans for high-value decisions or when encountering ambiguity.
Consumer Adoption Patterns
Consumer adoption of AI shopping agents is accelerating rapidly, though with important demographic and contextual variations. Research shows 84% of Gen Z respondents report using at least one AI tool, compared to 68% of Millennials, 54% of Gen X, and 40% of Boomers. This generational divide suggests adoption will continue accelerating as digital-native consumers represent increasing market share.
However, adoption patterns reveal important nuances. Consumers express much greater comfort with AI agents handling routine, low-stakes purchases than major buying decisions. Reordering household staples, finding best prices on commodities, or booking standard travel all demonstrate high acceptance. Conversely, consumers still prefer direct control for purchases with high financial stakes, significant emotional value, or complex requirements.
Trust and transparency emerge as critical adoption factors. Nearly 90% of consumers want brands to disclose when AI is used in recommendations or transactions, while 31% say they would abandon a product entirely if they couldn’t switch off AI features. This reflects tension between desire for AI convenience and concern about loss of control or manipulation.
User experience with AI shopping agents also varies significantly based on agent quality. Well-designed agents that accurately interpret intent, find genuinely suitable options, and complete transactions smoothly receive positive feedback. However, agents that misunderstand requests, recommend inappropriate products, or create friction in the buying process generate frustration that can slow adoption.
Disintermediation Risks for Brands and Retailers
The Shift from Direct Relationships to Mediated Commerce
Traditional e-commerce enabled brands and retailers to establish direct digital relationships with consumers. Brands invested heavily in creating engaging websites, mobile apps, and digital experiences to attract and retain customers. This direct relationship allowed brands to control their narratives, build emotional connections, and capture valuable first-party data about customer preferences and behaviors.
Agentic commerce threatens this model by inserting AI agents as powerful intermediaries between brands and consumers. Research emphasizes that “your customer may no longer be a human with a browser—it is just as likely to be an autonomous agent, acting on that customer’s behalf.” This intermediation fundamentally alters brand-consumer dynamics.
In the emerging model, agents aggregate and evaluate offerings across many brands based on user preferences and optimization criteria. Brands lose control over how their products are presented and evaluated. The beautiful product photography, carefully crafted messaging, and emotional storytelling that drove e-commerce success may become largely irrelevant if agents make decisions based primarily on specifications, pricing, and reviews.
This shift creates asymmetric information dynamics. AI agents can simultaneously query dozens of retailers, compare offers in real-time, and identify optimal values far more efficiently than human shoppers. For commoditized products or categories where differentiation is primarily feature or price-based, this creates intense competitive pressure as agents ruthlessly optimize for user-defined criteria.
BCG research warns that without swift intervention, retailers risk being “sidelined and reduced to mere background utilities in increasingly agent-controlled digital marketplaces.” When agents mediate most transactions, brands become interchangeable suppliers rather than distinct choices with which consumers develop relationships.
Loss of Customer Data and Insights
The shift to agentic commerce threatens another critical brand asset: customer data. Traditional e-commerce generated rich first-party data about browsing behavior, product preferences, cart abandonment, purchase patterns, and more. Brands used this data to personalize experiences, inform product development, and optimize marketing.
When AI agents mediate transactions, brands may lose visibility into individual customer behavior. Agents may anonymize requests, aggregate across multiple users, or simply provide insufficient detail for brands to extract meaningful insights. Research notes that in an agent-driven world, “brands become interchangeable suppliers rather than distinct choices.”
This data loss has multiple implications. Brands may struggle to identify emerging trends in customer preferences if they only see aggregated agent requests rather than individual customer behavior. Personalization capabilities diminish when brands can’t track individual customer journeys. Attribution of marketing effectiveness becomes difficult when agents rather than humans make purchase decisions.
Some organizations are exploring new approaches to maintain customer relationships despite agent intermediation. These include direct-to-consumer subscription models where ongoing relationships transcend individual transactions, loyalty programs that incentivize customers to declare preferences to brands directly, and value-added services that require ongoing brand interaction rather than simple transactional relationships.
Brand Equity and Differentiation Challenges
Perhaps the most fundamental challenge concerns brand equity itself. Traditional brand building has relied on emotional connections, aspirational positioning, and experiential differentiation. Luxury brands command premium prices not just for product quality but for the brand story and identity they represent.
AI agents, however, are “hyper-rational buyers” focused on “objective factors like price, specifications, availability, and reviews when making decisions. Not emotional connections or brand loyalty.” This presents existential challenges for brands whose value proposition depends on emotional or aspirational positioning rather than functional superiority.
Research suggests this may lead to market polarization. Brands with genuinely superior products, distinctive innovations, or exceptional value propositions may thrive as agents reliably identify and recommend them. Brands relying primarily on marketing, brand equity, or emotional connection may struggle as agents ruthlessly optimize for functional criteria and value.
For luxury and premium brands, this creates particular challenges. How do you communicate luxury, heritage, or craftsmanship to an AI agent that evaluates based primarily on specifications and reviews? Some luxury brands are exploring ways to make brand attributes machine-readable, but fundamental questions remain about whether emotional brand value can meaningfully transfer through agent intermediation.
The challenge extends to brand loyalty itself. Research indicates that traditional brand loyalty — consumers preferentially selecting familiar brands even when alternatives might offer better value — may erode in agent-mediated commerce. If agents optimize purely for user-defined criteria without brand preferences, established brands lose the advantage of familiarity and consumer inertia.
Margin Compression and Price Competition
Agentic commerce creates potential for intense price competition that could compress margins across many categories. When agents can instantly compare offerings across dozens or hundreds of retailers, price becomes a powerful differentiator. Retailers may feel pressure to offer lowest prices to ensure agent recommendations.
This dynamic particularly threatens categories where products are relatively commoditized and differentiation is difficult. Consumer electronics, household goods, and standardized products face greatest price pressure since agents can easily determine functional equivalence and optimize for price.
Research examining early agentic shopping implementations has documented agents defaulting to lowest-price options even when slightly more expensive alternatives offered meaningfully better features or value. This suggests that unless agents are sophisticated enough to perform nuanced value assessments, they may drive race-to-bottom pricing dynamics.
For retailers, margin compression could make it difficult to sustain operations, particularly for those whose business models depended on brand relationships, superior merchandising, or value-added services that agents don’t value. The shift could accelerate consolidation toward large-scale operators with sufficient volume and efficiency to sustain operations on thinner margins.
Some organizations are exploring differentiation strategies beyond price to ensure agent recommendations. These include unique product offerings not available through competitors, superior delivery and service that agents can quantitatively assess, comprehensive return policies and guarantees that reduce perceived risk, and exclusive access or early availability that creates non-price competitive advantages.
Zero-Click Commerce: The Ultimate Automation
Defining Zero-Click Commerce
Zero-click commerce represents the logical endpoint of agentic automation — transactions that occur with no explicit human input beyond initial preference configuration. In this model, AI agents monitor user needs, inventory levels, and market conditions, proactively making purchases when appropriate without requiring conscious user decisions.
The concept extends beyond simple subscription models where users pre-authorize recurring purchases. Zero-click commerce involves agents making discretionary purchasing decisions based on learned preferences and contextual signals. For example, an agent might automatically reorder groceries based on consumption patterns, purchase recommended replacements when products near end-of-life, or book travel arrangements based on calendar analysis and inferred intent.
Current implementations remain relatively simple. Amazon’s Dash Replenishment Service enables devices to automatically reorder consumables when supplies run low. Some financial services use AI agents to automatically rebalance investment portfolios or execute trades based on market conditions. However, these examples represent early steps toward more comprehensive zero-click models.
Fully realized zero-click commerce would see agents managing much broader purchasing portfolios. Agents could handle routine procurement across multiple categories, optimize recurring expenses like utilities and subscriptions, make discretionary purchases for gifts or experiences based on calendar events and relationships, and manage complex multi-product purchases like complete wardrobe updates or home renovation projects.
Consumer Acceptance and Control
Consumer acceptance of zero-click commerce varies dramatically based on purchase characteristics and individual comfort with automation. Research indicates consumers are much more accepting of autonomous purchasing for well-defined, routine needs than for discretionary or high-value decisions.
For true zero-click models to achieve broad adoption, several conditions appear necessary. First, high confidence in agent accuracy — consumers need assurance that agents will make choices they would have made themselves. Second, easily reversible decisions — ability to quickly undo or modify automated purchases reduces perceived risk. Third, transparent reasoning — understanding why agents made specific choices helps build trust and enables preference refinement.
Fourth, meaningful control mechanisms — consumers need ability to set boundaries, approve certain purchase categories before execution, or disable automation for specific contexts. Finally, demonstrated value — agents must deliver clear benefits in time savings, cost optimization, or other dimensions that compensate for loss of direct control.
Research suggests significant demographic variation in zero-click acceptance. Younger, more digitally native consumers express greater comfort with automated purchasing, while older demographics prefer maintaining direct control. Busy professionals with limited shopping time may embrace automation more readily than those for whom shopping is leisure activity.
Cultural factors also influence acceptance. Research on trust and AI adoption across different regions shows that comfort with automation varies significantly across cultures, with some exhibiting greater acceptance of algorithmic decision-making than others.
Economic and Market Implications
Zero-click commerce could profoundly reshape market dynamics. When significant purchase volume flows through automated agent decisions rather than conscious human choices, several transformations become likely.
Market efficiency could increase dramatically as agents optimize purchasing decisions based on comprehensive information and rational criteria. This could reduce inefficiencies from information asymmetry, impulsive buying, or suboptimal choices.
However, this efficiency may come with concerning implications for market diversity and consumer welfare. If agents optimize primarily for price or narrowly defined criteria, they might reduce market diversity by consolidating demand around a small number of “optimal” options. This could reduce incentives for innovation, differentiation, and niche products.
Zero-click models also raise questions about market manipulation. If agents make purchasing decisions, entities that can influence agent behavior gain substantial power. This creates incentives for merchant agents to develop strategies for influencing buyer agents, potentially through subtle framing, strategic pricing, or other mechanisms.
Regulatory questions arise around consumer protection in zero-click models. If purchases occur without explicit consumer authorization, who bears responsibility for disputes, returns, or fraudulent transactions? How can consumers maintain oversight of agent purchasing when volumes become large? What disclosure requirements should apply to agent-to-agent commerce?
Strategic Implications for Merchants and Brands
Optimizing for Agent Discovery and Recommendation
In agent-mediated commerce, brand success increasingly depends on appearing in agent recommendations. This requires new capabilities beyond traditional SEO and digital marketing. Research emphasizes that brands must focus on “speed, structure, and accessibility” since “unlike humans, AI agents prioritize machine-readability over visual design.”
Structured data becomes critical. Implementations of Schema.org markup, product APIs, and machine-readable formats make content visible in AI-driven results and recommendations. This represents fundamental shift from creating visually appealing websites to ensuring information is readily parseable by AI systems.
Comprehensive and accurate product information helps agents make confident recommendations. This includes detailed specifications, use cases, comparison data, customer reviews, and availability information. Incomplete or inaccurate data reduces likelihood of agent recommendations since agents prefer confidence in their suggestions.
Agent optimization requires different mindset than human-focused marketing. Research notes that “for optimizing online advertisements targeted at AI agents, textual content should be closely aligned with anticipated user queries and tasks. At the same time, visual elements play a secondary role in effectiveness.”
Some organizations are developing specialized “agent experience” teams focusing on optimization for AI discoverability. These teams work to ensure product information is structured appropriately, maintain comprehensive APIs, monitor how agents interact with their properties, and continuously refine based on agent behavior patterns.
API-First Commerce Strategies
The shift toward agent-mediated commerce necessitates API-first strategies where programmatic access to product data, inventory, pricing, and transaction capabilities becomes primary interface rather than human-facing websites.
API-first approaches involve several key elements. Comprehensive product catalogs expose complete product information programmatically. Real-time inventory and pricing APIs enable agents to make decisions based on current availability and cost. Transaction APIs handle order placement, payment processing, and fulfillment coordination. Agent-specific endpoints optimize for agent access patterns rather than human interaction flows.
Leading commerce platforms are investing heavily in API infrastructure. Shopify, for instance, has built extensive API capabilities enabling third-party developers and agents to access merchant data and transaction capabilities. Payment providers including Stripe, PayPal, Mastercard, and Visa have announced agent-specific payment solutions enabling secure, autonomous transactions.
The Agent Payments Protocol (AP2), an open payment-agnostic framework for secure agent-led transactions, has attracted support from major players including Mastercard and PayPal. These standardization efforts signal industry recognition that agent commerce requires different technical approaches than human-focused e-commerce.
Organizations pursuing API-first strategies face several implementation challenges. Legacy systems designed for human interaction may struggle to support high-velocity agent access. Security models must balance agent autonomy with fraud prevention and regulatory compliance. Performance and reliability requirements increase when agents expect consistent sub-second response times.
Building Direct Agent Relationships
While much discussion focuses on brand disintermediation, forward-thinking organizations are exploring how to establish direct relationships with consumer AI agents. Rather than viewing agents as threats, these brands treat them as a new customer category requiring distinct engagement strategies.
This involves several novel approaches. Brand APIs specifically designed for agent consumption provide structured access to product information, recommendations, and transaction capabilities. Agent-specific pricing and promotional strategies recognize that agents may evaluate offers differently than humans. Loyalty integration enables agents to factor existing customer relationships into decision-making.
Some premium brands are developing what might be termed “agent partners” — establishing preferential relationships with popular consumer agents to ensure consideration in recommendations. This mirrors historical relationships between brands and retailers but adapted for the agent era.
However, this approach faces challenges. Consumer advocacy groups have raised concerns about brands potentially “gaming” agent recommendations through special relationships rather than genuine merit. Regulators may scrutinize agent-brand partnerships for potential anti-competitive effects or consumer harm.
Maintaining Human Touchpoints
Despite agent mediation, many brands recognize value in maintaining direct human touchpoints. Not all commerce will immediately migrate to fully autonomous models, and human interaction provides opportunities for brand building that agent mediation cannot fully replace.
Successful multi-channel strategies combine agent optimization with preserved human experiences. This includes flagship stores and experiences where brand storytelling and emotional connection occur through direct human engagement. Community building around brands creates loyalty and engagement independent of transactional relationships. Content and education position brands as trusted advisors rather than mere product suppliers.
Some brands are developing explicit “agent-free” channels for customers who value human interaction in their shopping journey. Premium services, customization, and consultation represent areas where human expertise and interaction remain highly valued even as routine transactions migrate to agent automation.
Privacy, Security, and Trust Considerations
Agent Authentication and Authorization
As AI agents take on autonomous purchasing authority, robust authentication and authorization mechanisms become critical. Research emphasizes that “as agents act as autonomous entities on behalf of real users, digital identity, permission management, and secure payment infrastructure are paramount.”
Each agent requires unique credentials and time-limited identity tokens defining access scope and duration. Agents should only gain access to sensitive data or payment authority within predefined contexts, such as specific sessions or transaction categories. Role-scoped identity limits agent authority to specific operations, preventing agents from going “rogue.”
For example, an agent authorized to make a single purchase should not retain unfettered access to user finances afterward. Time-bound permissions, spending limits, and category restrictions help ensure agent behavior remains within intended boundaries.
Every agent action — from inventory queries to checkouts — should be logged for traceability, rollback capability, and compliance auditing. This audit trail becomes essential for dispute resolution, fraud detection, and regulatory compliance.
Data Privacy and Regulatory Compliance
Agentic commerce raises novel privacy questions. When agents act on behalf of consumers, what data do they share with merchants? How is that data protected? Who owns data generated through agent-to-agent transactions?
Compliance with regulations including GDPR, CCPA, and emerging AI-specific regulations requires careful consideration. Agents must obtain and manage appropriate consent for data sharing, implement robust data protection measures, provide transparency about data usage, and enable users to access, correct, or delete their data.
The EU’s AI Act represents the most ambitious regulatory response, though this legislation predates agentic commerce and lacks provisions specifically addressing autonomous purchasing agents. This regulatory vacuum forces businesses to operate through legal uncertainty, constructing compliance strategies from interpretations of existing frameworks rather than purpose-built rules.
Research notes that “regulators will inevitably need to fundamentally reconceptualise core legal principles — agency, consent, and fair commercial practice — to govern a marketplace increasingly dominated by algorithmic rather than human decision-making.”
Building and Maintaining Consumer Trust
Trust emerges as perhaps the most critical factor determining agentic commerce adoption and success. Multiple studies document that consumer trust in AI agents remains fragile, with 78% of Americans admitting it’s nearly impossible to separate real from machine-generated content online, and three-quarters saying they “trust the internet less than ever.”
Building trust requires multi-faceted approach. Transparency about agent capabilities and limitations helps calibrate user expectations. Clear disclosure when AI agents are involved in commerce transactions addresses desire for informed consent. Explainable decisions where agents articulate reasoning behind recommendations enable users to validate agent logic.
Meaningful control mechanisms allowing users to set boundaries, override decisions, or disable agent action preserve user autonomy. Consistent performance delivering reliable outcomes builds confidence through demonstrated competence. Effective recourse when agents make mistakes or users are dissatisfied maintains trust even when problems occur.
Research examining AI ad disclosure found that transparency increases consumer trust, with 72% of consumers reporting that clear disclosure about AI usage improves their comfort level. However, the same research revealed that 53% of consumers are unfamiliar with how companies use AI in advertisements, demonstrating significant education gap.
The Future Landscape of Commerce
Timeline and Adoption Trajectories
While agentic commerce represents transformative potential, actual adoption will likely unfold gradually with considerable variation across categories, demographics, and regions. Industry projections suggest several adoption phases.
Near-term (2025-2027): Simple agent-mediated transactions for routine purchases become increasingly common. Consumers use agents primarily for price comparison, product discovery, and recommendations while maintaining direct control over final purchase decisions. Early zero-click implementations handle consumable reordering and subscription management.
Medium-term (2027-2030): Agents manage larger portions of total household spending for amenable categories. Consumer comfort with agent autonomy grows based on demonstrated performance. Agent-to-agent commerce protocols mature and achieve broader adoption. Regulatory frameworks specifically addressing agentic commerce begin emerging.
Long-term (2030+): Agents mediate majority of routine commerce transactions. Zero-click models achieve mainstream acceptance for appropriate categories. The line between agent-mediated and human-controlled commerce blurs as sophisticated agents understand when to involve humans. New market structures emerge reflecting agent-first rather than human-first commerce.
However, certain categories will likely resist agent mediation. Experiential purchases, luxury goods, highly customized products, and emotionally significant buying decisions may remain predominantly human-driven even as routine transactions migrate to agent automation.
Competitive Dynamics and Market Structure
Agentic commerce will likely accelerate certain competitive trends while creating new strategic dimensions. Scale advantages may increase as large operators can invest in sophisticated agent integration, comprehensive APIs, and agent-optimized experiences. Small merchants lacking resources for agent optimization could face disadvantage.
However, agent mediation might also create opportunities for new entrants. If agents reliably identify high-quality, fairly-priced offerings regardless of brand recognition, excellent products from unknown sources could gain discovery that would have been impossible in human-dominated markets.
New intermediaries will likely emerge. Agent service providers help merchants optimize for agent discovery. Agent platforms compete to provide best consumer experiences. Data providers supply market intelligence about agent behavior and preferences.
Some research suggests potential for market concentration as agents optimize toward “best” options in each category, reducing the “long tail” of niche products. However, sophisticated agents capable of understanding diverse user preferences might actually increase market diversity by helping users discover specialized offerings aligned with their specific needs.
Conclusion: Navigating the Transformation
Agentic commerce represents neither inevitable utopia nor guaranteed dystopia. Rather, it offers new capabilities that will be shaped by technological advancement, consumer adoption patterns, merchant strategies, and regulatory frameworks that emerge over coming years.
For brands and retailers, the transformation presents both existential challenges and significant opportunities. Those that embrace agent optimization, invest in API-first strategies, maintain trust through transparency, and preserve direct consumer relationships where valuable will be best positioned for the emerging landscape.
For consumers, agentic commerce promises greater efficiency, better value optimization, and reduced friction in routine purchasing. However, these benefits come with questions about privacy, control, and whether agent optimization truly serves user interests or primarily benefits agent developers and merchant partners.
For society, agentic commerce raises fundamental questions about market structure, competition, consumer protection, and the appropriate role of AI in mediating human economic activity. The frameworks we develop for governing this transformation will shape commerce for decades to come.
The organizations thriving in this new era will be those that recognize agentic commerce not as a distant possibility but as an already-emerging reality requiring immediate strategic response. The time for abstract consideration has passed — the agent revolution in commerce has begun, and success requires action today.
References
This analysis draws on extensive research from industry reports, academic studies, and real-world implementations of agentic commerce systems. Key sources include reports from McKinsey, BCG, Adobe, and specialized commerce research firms, as well as technical documentation from payment providers and commerce platforms implementing agent-specific capabilities. All information reflects market understanding as of November 2025.
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