Table of Contents
Rye’s Universal Checkout API: Architecture for Agentic Commerce at Scale
Rye
Sep 4, 2025
A whitepaper detailing Rye's solution for fast, universal checkout, using browser automation, auto-extracted deterministic workflows, and anti-fraud mitigation.
1. Abstract
Agentic commerce requires autonomously completing checkout across heterogeneous merchant systems with minimal latency, high reliability, and strict handling of buyer payment data. Rye’s Universal Checkout API exposes a simple contract: product detail page (PDP) URL, buyer identity, and a third‑party–tokenized payment method in; confirmed merchant order and status out. It executes the transaction autonomously while insulating integrators from page variability, shipping/tax retrieval, and PCI DSS scope. The core technical challenge is resolving final price, shipping methods, and taxes fast enough that AI shopping and background agents remain competitive with native merchant flows; doing this robustly across arbitrary storefronts has historically forced human‑in‑the‑loop fulfillment or narrow merchant coverage.
Our system combines (i) a browser‑automation agent that can populate checkout flows to retrieve offers and submit orders, (ii) automatic extraction of successful traces into deterministic workflows to reduce token/latency costs, with self‑healing fallbacks when layouts change, and (iii) a fraud‑mitigation proxy layer (residential IP selection, geoproximity, human‑like interaction profiles) to avoid downstream merchant cancellations. Integrators never handle raw card data; the API uses tokenized payment methods today and is designed to interoperate with issuer‑backed proxy‑card protocols.
Measured on live traffic (Aug 2025), the system meets production-grade SLOs on major surfaces (Shopify/Amazon), while fully agentic browser flows are converging toward 35 s offer, 10 s checkout, and ~90% reliability on the near-term roadmap. These results provide a credible path to universal agentic checkout.
Agentic commerce requires autonomously completing checkout across heterogeneous merchant systems with minimal latency, high reliability, and strict handling of buyer payment data. Rye’s Universal Checkout API exposes a simple contract: product detail page (PDP) URL, buyer identity, and a third‑party–tokenized payment method in; confirmed merchant order and status out. It executes the transaction autonomously while insulating integrators from page variability, shipping/tax retrieval, and PCI DSS scope. The core technical challenge is resolving final price, shipping methods, and taxes fast enough that AI shopping and background agents remain competitive with native merchant flows; doing this robustly across arbitrary storefronts has historically forced human‑in‑the‑loop fulfillment or narrow merchant coverage.
Our system combines (i) a browser‑automation agent that can populate checkout flows to retrieve offers and submit orders, (ii) automatic extraction of successful traces into deterministic workflows to reduce token/latency costs, with self‑healing fallbacks when layouts change, and (iii) a fraud‑mitigation proxy layer (residential IP selection, geoproximity, human‑like interaction profiles) to avoid downstream merchant cancellations. Integrators never handle raw card data; the API uses tokenized payment methods today and is designed to interoperate with issuer‑backed proxy‑card protocols.
Measured on live traffic (Aug 2025), the system meets production-grade SLOs on major surfaces (Shopify/Amazon), while fully agentic browser flows are converging toward 35 s offer, 10 s checkout, and ~90% reliability on the near-term roadmap. These results provide a credible path to universal agentic checkout.
Agentic commerce requires autonomously completing checkout across heterogeneous merchant systems with minimal latency, high reliability, and strict handling of buyer payment data. Rye’s Universal Checkout API exposes a simple contract: product detail page (PDP) URL, buyer identity, and a third‑party–tokenized payment method in; confirmed merchant order and status out. It executes the transaction autonomously while insulating integrators from page variability, shipping/tax retrieval, and PCI DSS scope. The core technical challenge is resolving final price, shipping methods, and taxes fast enough that AI shopping and background agents remain competitive with native merchant flows; doing this robustly across arbitrary storefronts has historically forced human‑in‑the‑loop fulfillment or narrow merchant coverage.
Our system combines (i) a browser‑automation agent that can populate checkout flows to retrieve offers and submit orders, (ii) automatic extraction of successful traces into deterministic workflows to reduce token/latency costs, with self‑healing fallbacks when layouts change, and (iii) a fraud‑mitigation proxy layer (residential IP selection, geoproximity, human‑like interaction profiles) to avoid downstream merchant cancellations. Integrators never handle raw card data; the API uses tokenized payment methods today and is designed to interoperate with issuer‑backed proxy‑card protocols.
Measured on live traffic (Aug 2025), the system meets production-grade SLOs on major surfaces (Shopify/Amazon), while fully agentic browser flows are converging toward 35 s offer, 10 s checkout, and ~90% reliability on the near-term roadmap. These results provide a credible path to universal agentic checkout.
2. Introduction
Agentic commerce refers to AI-driven systems that can autonomously buy products or services on behalf of end users. Unlike conventional e-commerce, where a shopper manually selects a product, enters shipping details, and provides payment, agentic commerce requires an automated agent to handle this full transaction lifecycle reliably and at scale. This places stringent demands on latency, fault tolerance, and fraud resilience, since any failure in checkout undermines the viability of autonomous agents in production settings.
The core computational problem is programmatic checkout across heterogeneous merchant infrastructures. Each storefront presents unique product page structures, authentication flows, and anti-automation defenses. Naively applying browser automation frequently results in merchant-side cancellations due to fraud detection systems. At the same time, an effective agent must resolve final pricing, taxes, and shipping options with low latency—delays longer than native merchant checkout flows cause end users to abandon the agentic path.
Prior approaches have not solved this problem robustly. Some platforms lean heavily on manual fulfillment teams, introducing high cost and inconsistency. Others restrict merchant coverage, for example limiting integrations to Shopify, which undermines the universality required for AI shopping and discovery platforms.
Rye’s Universal Checkout API was designed to address these gaps. Its design goals are:
Universality: Checkout support for any merchant accessible via a product detail page (PDP) URL, not limited to a fixed partner network.
Low latency: >35s offer resolution and >10s checkout latency for all stores via AI-driven flows; >5s offer resolution and checkout latency for Amazon and Shopify via direct integrations
High reliability: >90% of all orders processed and confirmed with no errors.
Security and compliance: Isolation of raw payment data from AI agents via tokenization, reducing PCI DSS burden for integrators.
Minimal developer overhead: A contract consisting only of PDP URL, buyer identity, and a tokenized payment method in; order confirmation and tracking details out.
This whitepaper situates Rye’s system against the backdrop of agentic commerce, articulates the technical challenges that make the problem nontrivial, and presents an architecture designed for reliability, performance, and defensibility.
3. Problem Definition
Given a public product detail page (PDP) URL, buyer identity, and a tokenized payment method, our goal is to (a) return accurate total cost (item price, tax, shipping) under low latency and (b) submit the order to the merchant, producing a confirmed merchant order with high reliability. The developer integrating the API must never handle raw cardholder data.
Inputs:
productUrl: resolvable PDP URL for a physical good.buyer: shipping address and contact fields sufficient for the merchant to quote shipping/tax and fulfill.paymentMethod: a third‑party–tokenized instrument; the API accepts a token rather than primary account number (PAN), minimizing PCI burden for the integrator and isolating sensitive data from the AI execution layer.
Outputs:
Offer resolution: Structured set of shipping methods, taxes, and total cost, suitable for display or autonomous decisioning.
Order confirmation: Terminal state (
completedorfailed) for the checkout intent, with failure reasons when applicable; merchant order identifiers and status propagation are part of the interface contract.
Interaction model: The developer creates a checkout intent with the inputs above, polls until the intent reaches awaiting_confirmation (offer resolved), then confirms with the tokenized payment method. The developer polls the confirmed checkout intent until it reaches a terminal state.
3.1 Computational challenges
Universal, programmatic checkout is hard because the web is not uniform. Each merchant implements its own PDP structure, cart/checkout sequence, and mix of anti-automation controls, so an approach that works on one site will often fail on another. Any agentic system must therefore operate across this heterogeneity while still consistently meeting the user’s expectations of native e-commerce: fast, accurate totals and reliable orders.
3.1.1 Merchant fraud defenses
A central failure mode in universal checkout is merchant fraud systems cancelling legitimate automated orders. Systems that “look” automated at the network or interaction layer are routinely blocked. A viable design must therefore make automated sessions operationally indistinguishable from real shoppers (e.g., session setup, pacing, interaction characteristics).
3.1.2 Low-latency, full-cost resolution
Agentic checkout must surface the true landed cost—item price, shipping method/cost, and taxes—fast enough that users don’t abandon the merchant site. Achieving this requires driving the live checkout flow with a real destination address to materialize shipping and tax, rather than quoting SKU price alone. Practically, this is the hardest computational slice of the problem and the key differentiator versus price-only solutions.
3.1.3 Reliability under real-world variance
Real websites change: popups appear, fields shift, A/B tests roll out, and entire layouts are redesigned. A production web automation system needs explicit failure categorization, safe retry behavior, and a self-healing path when structure changes. Without this resilience loop, automation rates and SLOs degrade rapidly at scale.
3.2 The Merchant-of-Record (MoR) problem
The Merchant of Record (MoR) is the entity legally accountable for a transaction, bearing obligations for payment processing, tax remittance, refunds, chargebacks, and compliance. In standard e-commerce flows, this is the company operating the storefront.
In programmatic checkout architectures, however, MoR designation is often shifted upstream, requiring the integrating developer to become MoR. This approach imposes substantial overhead, including cross-jurisdictional sales tax registration and filing, liability for chargeback disputes, and operational responsibility for returns/refunds. For integrators, such obligations are often untenable.
3.3 Prior approaches and their limitations
Early attempts at universal checkout have tended to trade scalability for expediency. A common pattern is to route the transactions that fail the easy path or reach latency limits to human fulfillment agents. While this helps paper over automation gaps, it imposes hard ceilings on throughput, inflates unit economics, and makes any claim of programmatic reliability non-defensible; in practice we’ve seen reports of providers operating with the overwhelming majority of orders handled manually.
Another recurring shortcut is building to support one or a few of the biggest e-commerce systems, such as Shopify. This limitation simplifies engineering but undermines the premise of agentic shopping, which by definition must span the open web rather than a fixed partner network.
Finally, where automation is attempted, many systems quote only the product price at selection time and defer shipping and tax due to the challenge of low-latency, full-cost resolution. This shifts financial risk to the platform (which may choose to absorb the shipping cost) or degrades UX when the true total surfaces later.
Coupled with multi-minute agent runs and fragile flows that break on pop-ups or layout changes, these approaches fail to meet the combined requirements of universality, low latency, complete cost resolution, and merchant-compatible automation that agentic AI systems demand.
Given a public product detail page (PDP) URL, buyer identity, and a tokenized payment method, our goal is to (a) return accurate total cost (item price, tax, shipping) under low latency and (b) submit the order to the merchant, producing a confirmed merchant order with high reliability. The developer integrating the API must never handle raw cardholder data.
Inputs:
productUrl: resolvable PDP URL for a physical good.buyer: shipping address and contact fields sufficient for the merchant to quote shipping/tax and fulfill.paymentMethod: a third‑party–tokenized instrument; the API accepts a token rather than primary account number (PAN), minimizing PCI burden for the integrator and isolating sensitive data from the AI execution layer.
Outputs:
Offer resolution: Structured set of shipping methods, taxes, and total cost, suitable for display or autonomous decisioning.
Order confirmation: Terminal state (
completedorfailed) for the checkout intent, with failure reasons when applicable; merchant order identifiers and status propagation are part of the interface contract.
Interaction model: The developer creates a checkout intent with the inputs above, polls until the intent reaches awaiting_confirmation (offer resolved), then confirms with the tokenized payment method. The developer polls the confirmed checkout intent until it reaches a terminal state.
3.1 Computational challenges
Universal, programmatic checkout is hard because the web is not uniform. Each merchant implements its own PDP structure, cart/checkout sequence, and mix of anti-automation controls, so an approach that works on one site will often fail on another. Any agentic system must therefore operate across this heterogeneity while still consistently meeting the user’s expectations of native e-commerce: fast, accurate totals and reliable orders.
3.1.1 Merchant fraud defenses
A central failure mode in universal checkout is merchant fraud systems cancelling legitimate automated orders. Systems that “look” automated at the network or interaction layer are routinely blocked. A viable design must therefore make automated sessions operationally indistinguishable from real shoppers (e.g., session setup, pacing, interaction characteristics).
3.1.2 Low-latency, full-cost resolution
Agentic checkout must surface the true landed cost—item price, shipping method/cost, and taxes—fast enough that users don’t abandon the merchant site. Achieving this requires driving the live checkout flow with a real destination address to materialize shipping and tax, rather than quoting SKU price alone. Practically, this is the hardest computational slice of the problem and the key differentiator versus price-only solutions.
3.1.3 Reliability under real-world variance
Real websites change: popups appear, fields shift, A/B tests roll out, and entire layouts are redesigned. A production web automation system needs explicit failure categorization, safe retry behavior, and a self-healing path when structure changes. Without this resilience loop, automation rates and SLOs degrade rapidly at scale.
3.2 The Merchant-of-Record (MoR) problem
The Merchant of Record (MoR) is the entity legally accountable for a transaction, bearing obligations for payment processing, tax remittance, refunds, chargebacks, and compliance. In standard e-commerce flows, this is the company operating the storefront.
In programmatic checkout architectures, however, MoR designation is often shifted upstream, requiring the integrating developer to become MoR. This approach imposes substantial overhead, including cross-jurisdictional sales tax registration and filing, liability for chargeback disputes, and operational responsibility for returns/refunds. For integrators, such obligations are often untenable.
3.3 Prior approaches and their limitations
Early attempts at universal checkout have tended to trade scalability for expediency. A common pattern is to route the transactions that fail the easy path or reach latency limits to human fulfillment agents. While this helps paper over automation gaps, it imposes hard ceilings on throughput, inflates unit economics, and makes any claim of programmatic reliability non-defensible; in practice we’ve seen reports of providers operating with the overwhelming majority of orders handled manually.
Another recurring shortcut is building to support one or a few of the biggest e-commerce systems, such as Shopify. This limitation simplifies engineering but undermines the premise of agentic shopping, which by definition must span the open web rather than a fixed partner network.
Finally, where automation is attempted, many systems quote only the product price at selection time and defer shipping and tax due to the challenge of low-latency, full-cost resolution. This shifts financial risk to the platform (which may choose to absorb the shipping cost) or degrades UX when the true total surfaces later.
Coupled with multi-minute agent runs and fragile flows that break on pop-ups or layout changes, these approaches fail to meet the combined requirements of universality, low latency, complete cost resolution, and merchant-compatible automation that agentic AI systems demand.
Given a public product detail page (PDP) URL, buyer identity, and a tokenized payment method, our goal is to (a) return accurate total cost (item price, tax, shipping) under low latency and (b) submit the order to the merchant, producing a confirmed merchant order with high reliability. The developer integrating the API must never handle raw cardholder data.
Inputs:
productUrl: resolvable PDP URL for a physical good.buyer: shipping address and contact fields sufficient for the merchant to quote shipping/tax and fulfill.paymentMethod: a third‑party–tokenized instrument; the API accepts a token rather than primary account number (PAN), minimizing PCI burden for the integrator and isolating sensitive data from the AI execution layer.
Outputs:
Offer resolution: Structured set of shipping methods, taxes, and total cost, suitable for display or autonomous decisioning.
Order confirmation: Terminal state (
completedorfailed) for the checkout intent, with failure reasons when applicable; merchant order identifiers and status propagation are part of the interface contract.
Interaction model: The developer creates a checkout intent with the inputs above, polls until the intent reaches awaiting_confirmation (offer resolved), then confirms with the tokenized payment method. The developer polls the confirmed checkout intent until it reaches a terminal state.
3.1 Computational challenges
Universal, programmatic checkout is hard because the web is not uniform. Each merchant implements its own PDP structure, cart/checkout sequence, and mix of anti-automation controls, so an approach that works on one site will often fail on another. Any agentic system must therefore operate across this heterogeneity while still consistently meeting the user’s expectations of native e-commerce: fast, accurate totals and reliable orders.
3.1.1 Merchant fraud defenses
A central failure mode in universal checkout is merchant fraud systems cancelling legitimate automated orders. Systems that “look” automated at the network or interaction layer are routinely blocked. A viable design must therefore make automated sessions operationally indistinguishable from real shoppers (e.g., session setup, pacing, interaction characteristics).
3.1.2 Low-latency, full-cost resolution
Agentic checkout must surface the true landed cost—item price, shipping method/cost, and taxes—fast enough that users don’t abandon the merchant site. Achieving this requires driving the live checkout flow with a real destination address to materialize shipping and tax, rather than quoting SKU price alone. Practically, this is the hardest computational slice of the problem and the key differentiator versus price-only solutions.
3.1.3 Reliability under real-world variance
Real websites change: popups appear, fields shift, A/B tests roll out, and entire layouts are redesigned. A production web automation system needs explicit failure categorization, safe retry behavior, and a self-healing path when structure changes. Without this resilience loop, automation rates and SLOs degrade rapidly at scale.
3.2 The Merchant-of-Record (MoR) problem
The Merchant of Record (MoR) is the entity legally accountable for a transaction, bearing obligations for payment processing, tax remittance, refunds, chargebacks, and compliance. In standard e-commerce flows, this is the company operating the storefront.
In programmatic checkout architectures, however, MoR designation is often shifted upstream, requiring the integrating developer to become MoR. This approach imposes substantial overhead, including cross-jurisdictional sales tax registration and filing, liability for chargeback disputes, and operational responsibility for returns/refunds. For integrators, such obligations are often untenable.
3.3 Prior approaches and their limitations
Early attempts at universal checkout have tended to trade scalability for expediency. A common pattern is to route the transactions that fail the easy path or reach latency limits to human fulfillment agents. While this helps paper over automation gaps, it imposes hard ceilings on throughput, inflates unit economics, and makes any claim of programmatic reliability non-defensible; in practice we’ve seen reports of providers operating with the overwhelming majority of orders handled manually.
Another recurring shortcut is building to support one or a few of the biggest e-commerce systems, such as Shopify. This limitation simplifies engineering but undermines the premise of agentic shopping, which by definition must span the open web rather than a fixed partner network.
Finally, where automation is attempted, many systems quote only the product price at selection time and defer shipping and tax due to the challenge of low-latency, full-cost resolution. This shifts financial risk to the platform (which may choose to absorb the shipping cost) or degrades UX when the true total surfaces later.
Coupled with multi-minute agent runs and fragile flows that break on pop-ups or layout changes, these approaches fail to meet the combined requirements of universality, low latency, complete cost resolution, and merchant-compatible automation that agentic AI systems demand.
4. System Architecture
4.1 Components
API Layer (Checkout Intent Service): REST endpoints implement the two‑phase contract (intent creation → offer resolution → confirmation) plus polling for status changes.
Orchestration Core (AI Browser Agent): A headful browser agent drives PDP → cart → checkout to (i) validate the URL, (ii) fill buyer details to materialize shipping options and taxes, and (iii) retrieve an executable offer. Critically, the same browser session is reused between offer resolution and confirmation to avoid re‑navigation and improve latency.
Deterministic Workflow Cache: On a site’s first successful pass, the system extracts the agent’s action trace and compiles it into a deterministic workflow keyed to the domain/layout. Subsequent runs prefer this plan for speed and cost; if DOM drift or interstitials break execution, the runtime falls back to the agent for the failing step(s) and self‑heals the cached workflow.
DOM Normalization & UI Shielding: Before the agent/plan runs, a page filter suppresses or hides known disruptors (newsletter modals, region pickers, cookie banners), stabilizes selectors, and enforces human‑like pacing and input behavior to avoid bot heuristics that flag robotic typing/mouse movement.
Merchant Risk Adapter: All merchant interactions are routed through residential, geo‑proximal IPs selected against the buyer’s shipping region to minimize distance signals and automated‑traffic fingerprints. Browser launch parameters and interaction profiles are tuned to resemble consumer hardware and behavior.
Payments & PCI Boundary: The API accepts a tokenized payment method rather than PAN; the LLM/agent sees only opaque tokens. Internally, the system is engineered to operate with either (a) issuer‑backed proxy card schemes (Visa/Mastercard) or (b) a PCI‑compliant vaulting path, preserving strict separation of secrets from the agent layer.
Order Submission & Tracking: Upon confirmation, the runtime submits the order and returns a terminal state; identifiers for reconciliation and status propagation integrate with polling and webhook semantics.
Direct‑Integration Accelerators (select ecosystems): Where native API integrations exist (e.g., Shopify/Amazon), the system reuses these to optimize performance, while the agent path provides universal coverage elsewhere.
Manual Fulfillment Fallback: Contractors complete a small and declining percentage of checkouts where the agent fails to make progress or fails with a transient issue. As future work, manual fulfillment contractors’ web browsers will be monitored to generate data that equips the agent with an understanding of potential edge cases and failure modes.
4.2 End‑to‑end execution flow
Intent creation: Client POSTs PDP URL, quantity, and buyer identity (name, email, phone, address) to create a checkout intent; server acknowledges and begins background processing.
Offer resolution (background): The runtime validates the PDP, navigates cart/checkout, fills the buyer address, and extracts shipping methods, taxes, and the total. Client polls intent until
state="awaiting_confirmation"and can present options/costs or proceed autonomously under policy.Confirmation: Client POSTs a tokenized payment method to confirm.
Order placement (background): The runtime reuses the existing browser session to place the order on the merchant site. Client polls until terminal state (completed or failed) is reached.
Status propagation: Client continues polling and/or subscribes to webhooks for order lifecycle events; API health is published separately for operational visibility.
4.3 Reliability, failure handling, and self‑healing
A simple failure taxonomy accounts for the reality that web checkout flows drift, break, and vary by merchant. At runtime, every failed attempt is classified into: (i) transient issues (e.g., timeouts, slow elements), (ii) structural breakages (DOM/layout changes), and (iii) policy outcomes (e.g., a merchant-side cancel). Transients trigger bounded, jittered retries; structural errors escalate to targeted re-planning; policy outcomes surface deterministic error codes so upstream systems can make explicit decisions. This keeps the control loop predictable while avoiding infinite retries or silent degradations.
Self-healing is achieved by coupling an AI-driven “first pass” with plan extraction. The first successful run on a site produces an execution trace, which is compiled into a deterministic workflow keyed to the domain/layout and preferred on subsequent orders for speed and stability. When the site changes—an element moves, a form gains a field, a new interstitial appears—the runtime falls back to the agent for just the failing step(s), completes the order, and regenerates the cached plan so the next run reflects the new reality. In practice, this closes the loop between exploration (agent) and exploitation (plan) and is a major driver of our offer-latency targets.
Operational hardening reduces the number of failures the system must even classify. A DOM normalization/UI-shielding layer suppresses known disruptors (newsletter modals, region pickers, cookie banners) that routinely derail generic agents. In parallel, preserving session continuity (cookies, cart, and the same headful browser) from offer computation through confirmation eliminates redundant navigation and form-fill and reduces variance.
4.4 Payments & merchant‑of‑record (MoR) model
Because the transaction is performed directly on the merchant’s site, integrators never become MoR; the checkout completes with the merchant as MoR in the steady state, while the platform supports tokenized payment submission and is engineered to interoperate with issuer proxy cards as they mature.
4.5 Current scope & assumptions
The present release targets single‑item, physical goods shipped to US addresses, and assumes PDP URLs encode preselected variants (size/color) to enable direct add‑to‑cart without interactive optioning. These constraints simplify plan compilation and reduce agent branching, and are roadmap items rather than fundamental limits.
4.1 Components
API Layer (Checkout Intent Service): REST endpoints implement the two‑phase contract (intent creation → offer resolution → confirmation) plus polling for status changes.
Orchestration Core (AI Browser Agent): A headful browser agent drives PDP → cart → checkout to (i) validate the URL, (ii) fill buyer details to materialize shipping options and taxes, and (iii) retrieve an executable offer. Critically, the same browser session is reused between offer resolution and confirmation to avoid re‑navigation and improve latency.
Deterministic Workflow Cache: On a site’s first successful pass, the system extracts the agent’s action trace and compiles it into a deterministic workflow keyed to the domain/layout. Subsequent runs prefer this plan for speed and cost; if DOM drift or interstitials break execution, the runtime falls back to the agent for the failing step(s) and self‑heals the cached workflow.
DOM Normalization & UI Shielding: Before the agent/plan runs, a page filter suppresses or hides known disruptors (newsletter modals, region pickers, cookie banners), stabilizes selectors, and enforces human‑like pacing and input behavior to avoid bot heuristics that flag robotic typing/mouse movement.
Merchant Risk Adapter: All merchant interactions are routed through residential, geo‑proximal IPs selected against the buyer’s shipping region to minimize distance signals and automated‑traffic fingerprints. Browser launch parameters and interaction profiles are tuned to resemble consumer hardware and behavior.
Payments & PCI Boundary: The API accepts a tokenized payment method rather than PAN; the LLM/agent sees only opaque tokens. Internally, the system is engineered to operate with either (a) issuer‑backed proxy card schemes (Visa/Mastercard) or (b) a PCI‑compliant vaulting path, preserving strict separation of secrets from the agent layer.
Order Submission & Tracking: Upon confirmation, the runtime submits the order and returns a terminal state; identifiers for reconciliation and status propagation integrate with polling and webhook semantics.
Direct‑Integration Accelerators (select ecosystems): Where native API integrations exist (e.g., Shopify/Amazon), the system reuses these to optimize performance, while the agent path provides universal coverage elsewhere.
Manual Fulfillment Fallback: Contractors complete a small and declining percentage of checkouts where the agent fails to make progress or fails with a transient issue. As future work, manual fulfillment contractors’ web browsers will be monitored to generate data that equips the agent with an understanding of potential edge cases and failure modes.
4.2 End‑to‑end execution flow
Intent creation: Client POSTs PDP URL, quantity, and buyer identity (name, email, phone, address) to create a checkout intent; server acknowledges and begins background processing.
Offer resolution (background): The runtime validates the PDP, navigates cart/checkout, fills the buyer address, and extracts shipping methods, taxes, and the total. Client polls intent until
state="awaiting_confirmation"and can present options/costs or proceed autonomously under policy.Confirmation: Client POSTs a tokenized payment method to confirm.
Order placement (background): The runtime reuses the existing browser session to place the order on the merchant site. Client polls until terminal state (completed or failed) is reached.
Status propagation: Client continues polling and/or subscribes to webhooks for order lifecycle events; API health is published separately for operational visibility.
4.3 Reliability, failure handling, and self‑healing
A simple failure taxonomy accounts for the reality that web checkout flows drift, break, and vary by merchant. At runtime, every failed attempt is classified into: (i) transient issues (e.g., timeouts, slow elements), (ii) structural breakages (DOM/layout changes), and (iii) policy outcomes (e.g., a merchant-side cancel). Transients trigger bounded, jittered retries; structural errors escalate to targeted re-planning; policy outcomes surface deterministic error codes so upstream systems can make explicit decisions. This keeps the control loop predictable while avoiding infinite retries or silent degradations.
Self-healing is achieved by coupling an AI-driven “first pass” with plan extraction. The first successful run on a site produces an execution trace, which is compiled into a deterministic workflow keyed to the domain/layout and preferred on subsequent orders for speed and stability. When the site changes—an element moves, a form gains a field, a new interstitial appears—the runtime falls back to the agent for just the failing step(s), completes the order, and regenerates the cached plan so the next run reflects the new reality. In practice, this closes the loop between exploration (agent) and exploitation (plan) and is a major driver of our offer-latency targets.
Operational hardening reduces the number of failures the system must even classify. A DOM normalization/UI-shielding layer suppresses known disruptors (newsletter modals, region pickers, cookie banners) that routinely derail generic agents. In parallel, preserving session continuity (cookies, cart, and the same headful browser) from offer computation through confirmation eliminates redundant navigation and form-fill and reduces variance.
4.4 Payments & merchant‑of‑record (MoR) model
Because the transaction is performed directly on the merchant’s site, integrators never become MoR; the checkout completes with the merchant as MoR in the steady state, while the platform supports tokenized payment submission and is engineered to interoperate with issuer proxy cards as they mature.
4.5 Current scope & assumptions
The present release targets single‑item, physical goods shipped to US addresses, and assumes PDP URLs encode preselected variants (size/color) to enable direct add‑to‑cart without interactive optioning. These constraints simplify plan compilation and reduce agent branching, and are roadmap items rather than fundamental limits.
4.1 Components
API Layer (Checkout Intent Service): REST endpoints implement the two‑phase contract (intent creation → offer resolution → confirmation) plus polling for status changes.
Orchestration Core (AI Browser Agent): A headful browser agent drives PDP → cart → checkout to (i) validate the URL, (ii) fill buyer details to materialize shipping options and taxes, and (iii) retrieve an executable offer. Critically, the same browser session is reused between offer resolution and confirmation to avoid re‑navigation and improve latency.
Deterministic Workflow Cache: On a site’s first successful pass, the system extracts the agent’s action trace and compiles it into a deterministic workflow keyed to the domain/layout. Subsequent runs prefer this plan for speed and cost; if DOM drift or interstitials break execution, the runtime falls back to the agent for the failing step(s) and self‑heals the cached workflow.
DOM Normalization & UI Shielding: Before the agent/plan runs, a page filter suppresses or hides known disruptors (newsletter modals, region pickers, cookie banners), stabilizes selectors, and enforces human‑like pacing and input behavior to avoid bot heuristics that flag robotic typing/mouse movement.
Merchant Risk Adapter: All merchant interactions are routed through residential, geo‑proximal IPs selected against the buyer’s shipping region to minimize distance signals and automated‑traffic fingerprints. Browser launch parameters and interaction profiles are tuned to resemble consumer hardware and behavior.
Payments & PCI Boundary: The API accepts a tokenized payment method rather than PAN; the LLM/agent sees only opaque tokens. Internally, the system is engineered to operate with either (a) issuer‑backed proxy card schemes (Visa/Mastercard) or (b) a PCI‑compliant vaulting path, preserving strict separation of secrets from the agent layer.
Order Submission & Tracking: Upon confirmation, the runtime submits the order and returns a terminal state; identifiers for reconciliation and status propagation integrate with polling and webhook semantics.
Direct‑Integration Accelerators (select ecosystems): Where native API integrations exist (e.g., Shopify/Amazon), the system reuses these to optimize performance, while the agent path provides universal coverage elsewhere.
Manual Fulfillment Fallback: Contractors complete a small and declining percentage of checkouts where the agent fails to make progress or fails with a transient issue. As future work, manual fulfillment contractors’ web browsers will be monitored to generate data that equips the agent with an understanding of potential edge cases and failure modes.
4.2 End‑to‑end execution flow
Intent creation: Client POSTs PDP URL, quantity, and buyer identity (name, email, phone, address) to create a checkout intent; server acknowledges and begins background processing.
Offer resolution (background): The runtime validates the PDP, navigates cart/checkout, fills the buyer address, and extracts shipping methods, taxes, and the total. Client polls intent until
state="awaiting_confirmation"and can present options/costs or proceed autonomously under policy.Confirmation: Client POSTs a tokenized payment method to confirm.
Order placement (background): The runtime reuses the existing browser session to place the order on the merchant site. Client polls until terminal state (completed or failed) is reached.
Status propagation: Client continues polling and/or subscribes to webhooks for order lifecycle events; API health is published separately for operational visibility.
4.3 Reliability, failure handling, and self‑healing
A simple failure taxonomy accounts for the reality that web checkout flows drift, break, and vary by merchant. At runtime, every failed attempt is classified into: (i) transient issues (e.g., timeouts, slow elements), (ii) structural breakages (DOM/layout changes), and (iii) policy outcomes (e.g., a merchant-side cancel). Transients trigger bounded, jittered retries; structural errors escalate to targeted re-planning; policy outcomes surface deterministic error codes so upstream systems can make explicit decisions. This keeps the control loop predictable while avoiding infinite retries or silent degradations.
Self-healing is achieved by coupling an AI-driven “first pass” with plan extraction. The first successful run on a site produces an execution trace, which is compiled into a deterministic workflow keyed to the domain/layout and preferred on subsequent orders for speed and stability. When the site changes—an element moves, a form gains a field, a new interstitial appears—the runtime falls back to the agent for just the failing step(s), completes the order, and regenerates the cached plan so the next run reflects the new reality. In practice, this closes the loop between exploration (agent) and exploitation (plan) and is a major driver of our offer-latency targets.
Operational hardening reduces the number of failures the system must even classify. A DOM normalization/UI-shielding layer suppresses known disruptors (newsletter modals, region pickers, cookie banners) that routinely derail generic agents. In parallel, preserving session continuity (cookies, cart, and the same headful browser) from offer computation through confirmation eliminates redundant navigation and form-fill and reduces variance.
4.4 Payments & merchant‑of‑record (MoR) model
Because the transaction is performed directly on the merchant’s site, integrators never become MoR; the checkout completes with the merchant as MoR in the steady state, while the platform supports tokenized payment submission and is engineered to interoperate with issuer proxy cards as they mature.
4.5 Current scope & assumptions
The present release targets single‑item, physical goods shipped to US addresses, and assumes PDP URLs encode preselected variants (size/color) to enable direct add‑to‑cart without interactive optioning. These constraints simplify plan compilation and reduce agent branching, and are roadmap items rather than fundamental limits.
5. Performance Analysis
We evaluate the Universal Checkout API across four primary service KPIs: (1) Order Reliability—share of orders processed and confirmed without errors; (2) Offer Latency—mean time to return price, shipping options, and tax; (3) Checkout Latency—mean time from confirmation to order creation; and (4) Manual Fulfillment—share of successfully placed orders requiring human intervention. We also track API uptime. Unless noted, values are means; per‑merchant variability is expected. Uptime commitment is 99.9% monthly, excluding scheduled maintenance.
While checkout latency is important, offer latency is critical, as the end shopper can move forward to confirmation and payment and receive the results by email/text, for example.
5.1 Current external commitments (Aug 2025)
Published SLA, effective as of August 7, 2025:
Amazon: 99% order reliability; 5 s offer latency; 5 s checkout latency; 1% MF.
Shopify: 96% order reliability; 5 s offer latency; 20 s checkout latency; 5% MF.
AI flows (browser‑automation): 65% order reliability; 5 min offer latency; 5 min checkout latency; 35% MF.
These figures represent what integrators can expect today while we continue to optimize infrastructure and the AI orchestration layer.
5.2 Targets and trajectory
4 weeks post launch:
Order Reliability | Offer Latency | Checkout Latency | Manual Fulfillment | |
Amazon | 99% | 5s | 5s | 1% |
Shopify | 96% | 5s | 15s | 5% |
AI | 75% | 3m30s | 5m | 25% |
8 weeks post-launch target:
Order Reliability | Offer Latency | Checkout Latency | Manual Fulfillment | |
Amazon | 99% | 5s | 5s | 1% |
Shopify | 96% | 5s | 12s | 5% |
AI | 90% | 35s | 10s | 15% |
5.3 Measurement methodology
Collection: End‑to‑end traces for each checkout intent and confirmation, with server‑side timers bracketing (a) offer retrieval and (b) order submission; MF is emitted by the orchestration controller when a human task is enqueued.
Aggregation: Means are reported externally; internal dashboards track distributions and tail behavior, especially for Shopify checkout and AI flows where variance is widest.
Normalization: Performance is merchant‑dependent; evaluations use a representative set of reasonably performing stores to avoid skew from pathological sites.
Service availability: API uptime is monitored continuously and reported against the 99.9% objective.
We evaluate the Universal Checkout API across four primary service KPIs: (1) Order Reliability—share of orders processed and confirmed without errors; (2) Offer Latency—mean time to return price, shipping options, and tax; (3) Checkout Latency—mean time from confirmation to order creation; and (4) Manual Fulfillment—share of successfully placed orders requiring human intervention. We also track API uptime. Unless noted, values are means; per‑merchant variability is expected. Uptime commitment is 99.9% monthly, excluding scheduled maintenance.
While checkout latency is important, offer latency is critical, as the end shopper can move forward to confirmation and payment and receive the results by email/text, for example.
5.1 Current external commitments (Aug 2025)
Published SLA, effective as of August 7, 2025:
Amazon: 99% order reliability; 5 s offer latency; 5 s checkout latency; 1% MF.
Shopify: 96% order reliability; 5 s offer latency; 20 s checkout latency; 5% MF.
AI flows (browser‑automation): 65% order reliability; 5 min offer latency; 5 min checkout latency; 35% MF.
These figures represent what integrators can expect today while we continue to optimize infrastructure and the AI orchestration layer.
5.2 Targets and trajectory
4 weeks post launch:
Order Reliability | Offer Latency | Checkout Latency | Manual Fulfillment | |
Amazon | 99% | 5s | 5s | 1% |
Shopify | 96% | 5s | 15s | 5% |
AI | 75% | 3m30s | 5m | 25% |
8 weeks post-launch target:
Order Reliability | Offer Latency | Checkout Latency | Manual Fulfillment | |
Amazon | 99% | 5s | 5s | 1% |
Shopify | 96% | 5s | 12s | 5% |
AI | 90% | 35s | 10s | 15% |
5.3 Measurement methodology
Collection: End‑to‑end traces for each checkout intent and confirmation, with server‑side timers bracketing (a) offer retrieval and (b) order submission; MF is emitted by the orchestration controller when a human task is enqueued.
Aggregation: Means are reported externally; internal dashboards track distributions and tail behavior, especially for Shopify checkout and AI flows where variance is widest.
Normalization: Performance is merchant‑dependent; evaluations use a representative set of reasonably performing stores to avoid skew from pathological sites.
Service availability: API uptime is monitored continuously and reported against the 99.9% objective.
We evaluate the Universal Checkout API across four primary service KPIs: (1) Order Reliability—share of orders processed and confirmed without errors; (2) Offer Latency—mean time to return price, shipping options, and tax; (3) Checkout Latency—mean time from confirmation to order creation; and (4) Manual Fulfillment—share of successfully placed orders requiring human intervention. We also track API uptime. Unless noted, values are means; per‑merchant variability is expected. Uptime commitment is 99.9% monthly, excluding scheduled maintenance.
While checkout latency is important, offer latency is critical, as the end shopper can move forward to confirmation and payment and receive the results by email/text, for example.
5.1 Current external commitments (Aug 2025)
Published SLA, effective as of August 7, 2025:
Amazon: 99% order reliability; 5 s offer latency; 5 s checkout latency; 1% MF.
Shopify: 96% order reliability; 5 s offer latency; 20 s checkout latency; 5% MF.
AI flows (browser‑automation): 65% order reliability; 5 min offer latency; 5 min checkout latency; 35% MF.
These figures represent what integrators can expect today while we continue to optimize infrastructure and the AI orchestration layer.
5.2 Targets and trajectory
4 weeks post launch:
Order Reliability | Offer Latency | Checkout Latency | Manual Fulfillment | |
Amazon | 99% | 5s | 5s | 1% |
Shopify | 96% | 5s | 15s | 5% |
AI | 75% | 3m30s | 5m | 25% |
8 weeks post-launch target:
Order Reliability | Offer Latency | Checkout Latency | Manual Fulfillment | |
Amazon | 99% | 5s | 5s | 1% |
Shopify | 96% | 5s | 12s | 5% |
AI | 90% | 35s | 10s | 15% |
5.3 Measurement methodology
Collection: End‑to‑end traces for each checkout intent and confirmation, with server‑side timers bracketing (a) offer retrieval and (b) order submission; MF is emitted by the orchestration controller when a human task is enqueued.
Aggregation: Means are reported externally; internal dashboards track distributions and tail behavior, especially for Shopify checkout and AI flows where variance is widest.
Normalization: Performance is merchant‑dependent; evaluations use a representative set of reasonably performing stores to avoid skew from pathological sites.
Service availability: API uptime is monitored continuously and reported against the 99.9% objective.
6. Security and Compliance
6.1 PCI DSS scope reduction via tokenization
Developers integrating Universal Checkout never handle raw cardholder data (CHD), nor does the LLM. Rye uses third-party, PCI-compliant payment providers to collect and vault sensitive data. The client generates a card token with the payment provider and supplies that token when confirming the checkout intent (e.g., type: "stripe_token").
When the LLM emits a plan to key in the credit card number, it only ever sees this opaque token; in this way, we prevent LLMs and their providers from accessing (and potentially storing) cardholder data. Non-AI code is responsible for entering the raw payment details into the payment form. As they become available, Rye will leverage card issuers' agentic payment rails for this purpose (e.g. Visa Intelligent Commerce / Mastercard Agent Pay).
Because the integrator only handles opaque tokens (never PAN/SAD), their PCI obligations are correspondingly minimized (e.g., to the lightest self-assessment questionnaire (SAQ) category applicable to pure token use in their environment). Within Rye, tokens are treated as high-sensitivity secrets: they are accepted only on confirm endpoints, stored only transiently to complete authorization, excluded from logs/metrics, and access-controlled.
6.2 Data minimization for buyer PII
Universal Checkout accepts only the buyer attributes strictly required to compute offers (taxes, shipping) and complete fulfillment—e.g., name, email, phone, and shipping address fields used in the buyer object. We do not request unrelated identifiers.
Buyer PII is used only to: (i) resolve shipping options and total landed cost; and (ii) populate merchant checkout/fulfillment. PII is retained only for the order lifecycle (offer computation → checkout → post-order notifications) with strict TTLs; event logs contain pseudonymous identifiers, not PII. Rye neither establishes nor maintains a relationship with the shopper, explicitly limiting its role to executing a one-off transaction.
The Universal Checkout API adopts an isolation model that mirrors CaMeL-style defenses. The system separates the privileged, tool-calling planner from components that ingest untrusted page content; extracts the intended control/data flows from the trusted task; and enforces fine-grained policy checks at tool-call boundaries (e.g., address autofill, payment submission). This matches CaMeL’s design, which wraps the LLM with a protective layer, explicitly derives control and data flows from the trusted query, and uses capability metadata to prevent exfiltration across unauthorized flows. PII handling is isolated from general compute via service-level boundaries, least-privilege IAM, and encryption in transit/at rest; only the minimal set of services with an explicit “allowed readers” capability can touch each field.
6.1 PCI DSS scope reduction via tokenization
Developers integrating Universal Checkout never handle raw cardholder data (CHD), nor does the LLM. Rye uses third-party, PCI-compliant payment providers to collect and vault sensitive data. The client generates a card token with the payment provider and supplies that token when confirming the checkout intent (e.g., type: "stripe_token").
When the LLM emits a plan to key in the credit card number, it only ever sees this opaque token; in this way, we prevent LLMs and their providers from accessing (and potentially storing) cardholder data. Non-AI code is responsible for entering the raw payment details into the payment form. As they become available, Rye will leverage card issuers' agentic payment rails for this purpose (e.g. Visa Intelligent Commerce / Mastercard Agent Pay).
Because the integrator only handles opaque tokens (never PAN/SAD), their PCI obligations are correspondingly minimized (e.g., to the lightest self-assessment questionnaire (SAQ) category applicable to pure token use in their environment). Within Rye, tokens are treated as high-sensitivity secrets: they are accepted only on confirm endpoints, stored only transiently to complete authorization, excluded from logs/metrics, and access-controlled.
6.2 Data minimization for buyer PII
Universal Checkout accepts only the buyer attributes strictly required to compute offers (taxes, shipping) and complete fulfillment—e.g., name, email, phone, and shipping address fields used in the buyer object. We do not request unrelated identifiers.
Buyer PII is used only to: (i) resolve shipping options and total landed cost; and (ii) populate merchant checkout/fulfillment. PII is retained only for the order lifecycle (offer computation → checkout → post-order notifications) with strict TTLs; event logs contain pseudonymous identifiers, not PII. Rye neither establishes nor maintains a relationship with the shopper, explicitly limiting its role to executing a one-off transaction.
The Universal Checkout API adopts an isolation model that mirrors CaMeL-style defenses. The system separates the privileged, tool-calling planner from components that ingest untrusted page content; extracts the intended control/data flows from the trusted task; and enforces fine-grained policy checks at tool-call boundaries (e.g., address autofill, payment submission). This matches CaMeL’s design, which wraps the LLM with a protective layer, explicitly derives control and data flows from the trusted query, and uses capability metadata to prevent exfiltration across unauthorized flows. PII handling is isolated from general compute via service-level boundaries, least-privilege IAM, and encryption in transit/at rest; only the minimal set of services with an explicit “allowed readers” capability can touch each field.
6.1 PCI DSS scope reduction via tokenization
Developers integrating Universal Checkout never handle raw cardholder data (CHD), nor does the LLM. Rye uses third-party, PCI-compliant payment providers to collect and vault sensitive data. The client generates a card token with the payment provider and supplies that token when confirming the checkout intent (e.g., type: "stripe_token").
When the LLM emits a plan to key in the credit card number, it only ever sees this opaque token; in this way, we prevent LLMs and their providers from accessing (and potentially storing) cardholder data. Non-AI code is responsible for entering the raw payment details into the payment form. As they become available, Rye will leverage card issuers' agentic payment rails for this purpose (e.g. Visa Intelligent Commerce / Mastercard Agent Pay).
Because the integrator only handles opaque tokens (never PAN/SAD), their PCI obligations are correspondingly minimized (e.g., to the lightest self-assessment questionnaire (SAQ) category applicable to pure token use in their environment). Within Rye, tokens are treated as high-sensitivity secrets: they are accepted only on confirm endpoints, stored only transiently to complete authorization, excluded from logs/metrics, and access-controlled.
6.2 Data minimization for buyer PII
Universal Checkout accepts only the buyer attributes strictly required to compute offers (taxes, shipping) and complete fulfillment—e.g., name, email, phone, and shipping address fields used in the buyer object. We do not request unrelated identifiers.
Buyer PII is used only to: (i) resolve shipping options and total landed cost; and (ii) populate merchant checkout/fulfillment. PII is retained only for the order lifecycle (offer computation → checkout → post-order notifications) with strict TTLs; event logs contain pseudonymous identifiers, not PII. Rye neither establishes nor maintains a relationship with the shopper, explicitly limiting its role to executing a one-off transaction.
The Universal Checkout API adopts an isolation model that mirrors CaMeL-style defenses. The system separates the privileged, tool-calling planner from components that ingest untrusted page content; extracts the intended control/data flows from the trusted task; and enforces fine-grained policy checks at tool-call boundaries (e.g., address autofill, payment submission). This matches CaMeL’s design, which wraps the LLM with a protective layer, explicitly derives control and data flows from the trusted query, and uses capability metadata to prevent exfiltration across unauthorized flows. PII handling is isolated from general compute via service-level boundaries, least-privilege IAM, and encryption in transit/at rest; only the minimal set of services with an explicit “allowed readers” capability can touch each field.
7. Conclusion
Rye’s Universal Checkout API addresses the central technical hurdle in agentic commerce: executing low-latency, high-reliability checkout across heterogeneous merchant systems using a minimal I/O contract. Developers provide only a PDP URL and buyer identity to create a checkout intent, poll until the system resolves price, shipping, and tax, then confirm using a tokenized payment method; the platform returns a terminal state and order status for downstream handling.
This contract keeps the merchant, not the developer, as merchant of record, while the system resolves shipping methods, taxes, and the final total directly from the live checkout flow so quotes reflect true landed cost. In doing so, it abstracts the main operational hurdles—variant resolution, address-dependent shipping, tax calculation, and fraud-sensitive checkout execution—without shifting risk or compliance burden onto the integrator.
The system’s architecture—an AI-driven browser orchestration layer that reuses sessions and turns successful runs into cached deterministic plans—yields a path to universality without sacrificing performance. Current external commitments and internal KPIs align: Amazon and Shopify transactions operate at sub-second to tens-of-seconds latencies with high order reliability, while AI (browser) flows have clear targets to converge to 35s offer and 10s checkout with materially higher automation rates. Service availability is backed by a 99.9% uptime objective and documented health endpoints.
Security and compliance choices are deliberate: payments are tokenized end-to-end (no PAN/SAD in developer or agent paths), buyer PII is limited to what’s required for quoting and fulfillment, and stateful APIs expose deterministic transitions for auditability.
Near-term scope is intentionally bounded (single-item orders, pre-selected variants via URL, US shipping only) to stabilize latency and reliability while the platform expands; multi-item carts, broader geography, and richer post-order telemetry are active roadmap items.
We invite interested engineering teams to (i) validate performance against their merchant sets, (ii) review the API interaction model and observability hooks, and (iii) assess compliance boundaries for integration at scale. The Universal Checkout API’s combination of minimal integration, measurable SLOs, and defensible isolation around payments and PII provides a credible foundation for agentic commerce in production.
Rye’s Universal Checkout API addresses the central technical hurdle in agentic commerce: executing low-latency, high-reliability checkout across heterogeneous merchant systems using a minimal I/O contract. Developers provide only a PDP URL and buyer identity to create a checkout intent, poll until the system resolves price, shipping, and tax, then confirm using a tokenized payment method; the platform returns a terminal state and order status for downstream handling.
This contract keeps the merchant, not the developer, as merchant of record, while the system resolves shipping methods, taxes, and the final total directly from the live checkout flow so quotes reflect true landed cost. In doing so, it abstracts the main operational hurdles—variant resolution, address-dependent shipping, tax calculation, and fraud-sensitive checkout execution—without shifting risk or compliance burden onto the integrator.
The system’s architecture—an AI-driven browser orchestration layer that reuses sessions and turns successful runs into cached deterministic plans—yields a path to universality without sacrificing performance. Current external commitments and internal KPIs align: Amazon and Shopify transactions operate at sub-second to tens-of-seconds latencies with high order reliability, while AI (browser) flows have clear targets to converge to 35s offer and 10s checkout with materially higher automation rates. Service availability is backed by a 99.9% uptime objective and documented health endpoints.
Security and compliance choices are deliberate: payments are tokenized end-to-end (no PAN/SAD in developer or agent paths), buyer PII is limited to what’s required for quoting and fulfillment, and stateful APIs expose deterministic transitions for auditability.
Near-term scope is intentionally bounded (single-item orders, pre-selected variants via URL, US shipping only) to stabilize latency and reliability while the platform expands; multi-item carts, broader geography, and richer post-order telemetry are active roadmap items.
We invite interested engineering teams to (i) validate performance against their merchant sets, (ii) review the API interaction model and observability hooks, and (iii) assess compliance boundaries for integration at scale. The Universal Checkout API’s combination of minimal integration, measurable SLOs, and defensible isolation around payments and PII provides a credible foundation for agentic commerce in production.
Rye’s Universal Checkout API addresses the central technical hurdle in agentic commerce: executing low-latency, high-reliability checkout across heterogeneous merchant systems using a minimal I/O contract. Developers provide only a PDP URL and buyer identity to create a checkout intent, poll until the system resolves price, shipping, and tax, then confirm using a tokenized payment method; the platform returns a terminal state and order status for downstream handling.
This contract keeps the merchant, not the developer, as merchant of record, while the system resolves shipping methods, taxes, and the final total directly from the live checkout flow so quotes reflect true landed cost. In doing so, it abstracts the main operational hurdles—variant resolution, address-dependent shipping, tax calculation, and fraud-sensitive checkout execution—without shifting risk or compliance burden onto the integrator.
The system’s architecture—an AI-driven browser orchestration layer that reuses sessions and turns successful runs into cached deterministic plans—yields a path to universality without sacrificing performance. Current external commitments and internal KPIs align: Amazon and Shopify transactions operate at sub-second to tens-of-seconds latencies with high order reliability, while AI (browser) flows have clear targets to converge to 35s offer and 10s checkout with materially higher automation rates. Service availability is backed by a 99.9% uptime objective and documented health endpoints.
Security and compliance choices are deliberate: payments are tokenized end-to-end (no PAN/SAD in developer or agent paths), buyer PII is limited to what’s required for quoting and fulfillment, and stateful APIs expose deterministic transitions for auditability.
Near-term scope is intentionally bounded (single-item orders, pre-selected variants via URL, US shipping only) to stabilize latency and reliability while the platform expands; multi-item carts, broader geography, and richer post-order telemetry are active roadmap items.
We invite interested engineering teams to (i) validate performance against their merchant sets, (ii) review the API interaction model and observability hooks, and (iii) assess compliance boundaries for integration at scale. The Universal Checkout API’s combination of minimal integration, measurable SLOs, and defensible isolation around payments and PII provides a credible foundation for agentic commerce in production.
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