Editor’s Note
This article was adapted by EUVC from an original insight published by Synaptic.
Agentic commerce promises to move online shopping from recommendation to execution. Instead of merely suggesting a product, an AI agent could discover options, negotiate, manage a subscription and complete a purchase on a user’s behalf.
The commercial promise is large, but the technology is not yet matching the narrative. Synaptic argues that consumer-facing adoption has run ahead of commercial readiness: product data remains inconsistent, merchant systems are difficult for agents to use, payment flows are fragile, and the rules for identity, consent and liability are still unsettled.
That gap is shaping the market. The most credible near-term value is moving towards the infrastructure beneath the shopping experience: interoperability protocols, agent-readable catalogues, payment and identity rails, fraud controls, and vertical agents operating in narrower, more structured purchasing environments.
Key takeaways
Consumer interest is rising faster than end-to-end agentic conversion.
Protocols are competing to become the common language between agents, merchants and payment systems.
Large merchants are likely to support several protocols rather than commit to a single ecosystem.
The most active startup opportunities are in payments, identity, fraud prevention, merchant enablement and vertical agents.
Distribution and incumbent partnerships can matter more than the size of a funding round.
Trust, permissioning, liability and regulation remain prerequisites for broad consumer adoption.
What is agentic commerce?
Agentic commerce is the use of AI agents to perform parts of a commercial journey on behalf of a person or business. At the more autonomous end of the spectrum, an agent does more than search or recommend: it can take action across several steps, including comparing products, assembling a basket, authenticating a user and initiating payment.
That distinction matters. Many products described as agentic are still advanced assistants or rules-based automations. They can help a user decide, but they cannot reliably complete a transaction across fragmented merchant systems without human intervention.
For additional context on where assistance becomes agency, see the related article Consumer AI agents: Ready for take off? It explores when consumers may trust an AI system to act independently rather than simply help them choose.
The market opportunity is large, but conversion remains limited
Market forecasts explain the intensity of interest. McKinsey estimates that agentic commerce could orchestrate $3 trillion to $5 trillion in global revenue by 2030, including up to $1 trillion in US business-to-consumer retail. Grand View Research estimates a $7.7 billion agentic commerce market in 2026.
Discovery is already changing. Adobe reported that generative AI traffic to US retail sites rose more than 4,000% year on year in July 2025. Yet traffic does not equal autonomous purchasing. Synaptic points to negligible agentic conversion and the retreat or redesign of early checkout products as evidence that consumer demand has not yet translated into reliable transaction execution.
The central mismatch is between what agents can promise and what commerce systems can support. Agents need structured, current product information; secure ways to authenticate themselves and the person they represent; payment rails that understand delegated authority; and clear rules for resolving errors. Most merchant stacks were designed for a human clicking through a website, not software acting under a standing mandate.
Which protocols are shaping agentic commerce?
Agentic commerce needs a common language between agents, merchants and payment systems. Several overlapping standards are competing to provide it. They are not perfect substitutes: some focus on commerce workflows, while others provide supporting context, tool access or agent-to-agent communication.
Protocol | Primary backers | Primary role | Strategic position |
|---|---|---|---|
ACP | OpenAI and Stripe | Checkout and transaction execution | Strongest around conversational checkout |
UCP | Google with a broad retail coalition | The shopping journey from discovery through post-purchase | Synaptic sees an early lead through retailer reach |
MCP | Anthropic and the open-source ecosystem | Secure context sharing and tool integration | A supporting standard for agent capabilities |
AP2 and A2A | Google and payment or technology partners | Agent payments and agent-to-agent interoperability | Supporting primitives for trusted multi-agent flows |
Synaptic argues that Google’s Universal Commerce Protocol has taken an early lead because it covers more of the shopping journey and has attracted a broad retail coalition. OpenAI and Stripe remain well placed in checkout execution, while the Model Context Protocol is emerging as an important open foundation for connecting agents to tools and data.
Large merchants have little incentive to choose a single standard this early. Supporting both UCP and ACP reduces platform dependence, even though it increases integration cost. Payment networks are similarly positioned to work across protocols: their advantage comes from remaining useful regardless of which interface controls the customer relationship.
Who controls the customer relationship?
The protocol contest is also a contest over discovery, trust and purchasing behaviour. If an agent runtime becomes the new browser, the agent provider can mediate discovery and potentially charge merchants for access or placement. If it behaves more like a search box, merchants retain more control and the runtime becomes easier to replace.
Google is positioned on both sides through its consumer interfaces and merchant relationships. OpenAI and Stripe are concentrating on execution. Anthropic is emphasising open, secure foundations. Neutral open-source initiatives add another route intended to limit proprietary capture of the transaction layer.
The five layers of the agentic commerce stack
Synaptic divides the emerging stack into five layers. The boundaries are still fluid, but the framework clarifies where incumbents have structural advantages and where startups may be able to build defensible infrastructure.
1. Agent runtimes
Agent runtimes are the consumer-facing systems that initiate shopping tasks. Synaptic’s May 2026 overview points to products from OpenAI, Perplexity, Anthropic, Manus and Genspark. This layer can own the interface and the user’s intent, but it depends on the rest of the stack to complete a transaction reliably.
2. Payment and transaction rails
This layer handles delegated authorisation, checkout and settlement. Stripe, Visa, Mastercard and PayPal are adapting existing networks, while startups are building agent-native payment primitives. The likely pattern is partnership and acquisition: incumbents bring trust, licences and distribution; startups move faster on the new technical requirements.
A related article provides additional context on how AI, stablecoins and contextual data are changing B2B payment infrastructure.
3. Vertical agents
Vertical agents focus on one type of purchase, such as travel, groceries, procurement, fashion or advertising. Their scope is narrower, but they can control more of the workflow and operate against better-structured data. Synaptic sees this as a path to higher conversion and more durable, software-like retention.
For adjacent thinking on the organisational implications, see the related article From HustleGPT to autonomous brands, which considers how networks of agents could reshape retail operations beyond the checkout itself.
4. Trust, identity and fraud
Merchants need to distinguish an authorised agent from a malicious bot. That requires agent identity, transaction verification and evidence that a purchase reflects the user’s intent. Existing fraud systems were designed around human behaviour and may not recognise risks such as prompt injection, agent impersonation or coordinated synthetic-shopping activity.
5. Merchant enablement
Merchant enablement makes a storefront machine-readable. Structured product catalogues, current inventory, agent-accessible checkout flows and tool servers allow an agent to browse, compare and buy.
Synaptic treats this as foundational: poor product data prevents every layer above it from working. Yet the category has attracted less startup funding than its importance might suggest.
Where startups are finding room to compete
The market remains symbiotic rather than purely disruptive. Incumbents own merchant reach, regulated payment infrastructure and established trust. Startups are filling the gaps where legacy systems move more slowly.
Agent-native identity and payments: Verifying non-human actors and enforcing spending authority.
Universal checkout: Connecting agents to fragmented merchants and reducing failure at the final transaction step.
Merchant optimisation: Turning catalogues, inventory and checkout into interfaces that agents can understand and use.
Vertical workflows: Controlling a complete transaction in domains such as travel and B2B procurement.
Synaptic identifies trust, checkout and identity as the emerging battlegrounds. It also notes a persistent shortage of checkout startups despite the category’s importance: agents may discover and compare effectively, then fail because of fraud controls, fragmented merchant systems or incomplete payment integration.
Funding is following infrastructure and distribution
Capital has concentrated in payments, identity and infrastructure. Synaptic identifies three routes: dedicated rounds for agent-to-merchant infrastructure, strategic acquisitions by incumbents, and large financings for general-purpose agent runtimes in which commerce is one monetisation path among several.
The size of a round is not the only signal. A startup with strong distribution, industry relationships or a launch partnership can move faster than a better-funded competitor that lacks access to merchants or networks. This is particularly true in payments, where integration and trust are difficult to build from the outside.
Consolidation is already visible in the rails layer. Examples in Synaptic’s overview include Stripe’s completed acquisition of Bridge, PayPal’s agreement to acquire Cymbio and Mastercard’s announced acquisition of BVNK.
Together, these moves support the view that established networks will buy or partner for capabilities that would take longer to build internally.
Four challenges must be solved before mass adoption
Protocol fragmentation. A merchant that supports one protocol is not automatically available to an agent using another. Supporting several standards improves reach but raises integration and maintenance costs.
Regulation and accountability. Synaptic expects disclosure, consent and accountability requirements to become more important as agents act with greater autonomy. Existing consumer-protection rules already matter, while the application of AI-specific regulation will depend on the system and its use case.
Liability, chargebacks and errors. The industry has no settled playbook for an agent that buys the wrong item, accepts the wrong price or acts on fabricated product information. Responsibility may be contested among the merchant, agent provider, payment network, issuer and user.
Permissioning and transparency. Broad, one-time mandates such as “buy anything under this amount” are unlikely to be sufficient for every purchase. Consumers need permissions that are granular, contextual and easy to revoke, together with a clear record of what the agent was authorised to do.
What the market map suggests for investors and operators
The near-term opportunity is not simply to build a better shopping assistant. It is to remove the structural reasons agents cannot transact reliably. The strongest positions may belong to companies that become shared infrastructure across several merchants, protocols and agent runtimes.
For investors, that shifts attention towards integration depth, distribution, regulatory readiness and the quality of merchant data. For operators, it means becoming agent-readable without surrendering the customer relationship to a single platform.
For both groups, the core question is whether a product can help authorised agents complete a real transaction safely, not whether it can produce an impressive demonstration.
Synaptic’s conclusion is deliberately cautious: agentic commerce remains an important long-term bet, but infrastructure and trust are likely to mature before autonomous consumer purchasing becomes routine.
Frequently asked questions
What is the difference between an AI shopping assistant and an agent?
An assistant helps a person search, compare or decide. An agent can also take action on the person’s behalf, potentially across several steps of a transaction. In practice, many products sit between those two ends of the spectrum.
Which agentic commerce protocol is most likely to win?
The market is too early for a definitive winner. Synaptic sees UCP as an early leader in broad retail workflows, ACP as strong in checkout execution and MCP as an important open layer for tools and context. Large merchants are likely to support multiple standards while the market develops.
Where are the strongest startup opportunities?
The overview points to identity, fraud prevention, payment infrastructure, universal checkout, merchant enablement and vertical agents. These are areas where agent-specific technical needs are emerging faster than many incumbent systems can adapt.
Why has consumer adoption lagged behind the hype?
End-to-end transactions depend on accurate product data, agent-accessible merchant systems, delegated payment authority, fraud controls and consumer trust. Weakness in any one of these layers can stop the purchase even when product discovery works well.
About the original author
Synaptic is a private-market data platform that turns private-company signals, including financials, company materials, websites, news and alternative metrics, into structured and connected data for investors.
Learn more about Synaptic’s work supporting data-driven private-market research.
Source
This article was adapted by EUVC from an original article published by Synaptic.
