I. Foundations of Agentic Commerce
What is Agentic Commerce?
Agentic Commerce is the next evolution of digital commerce where AI agents like ChatGPT, Gemini, Perplexity, and Copilot research, recommend, and even purchase products on behalf of users.
Agentic Commerce brings together the research and decision-making steps consumers used to perform across search engines, comparison sites, social reviews and in-store interactions into a single query to a large language model (LLM). Instead of browsing multiple sites or reviewing countless user posts, a shopper asks an agent, “What’s the best LED bulb to replace this lamp?” and the agent considers dozens of sources (product specs, reviews, inventory, pricing, fulfillment details) then recommends a curated list.
LLMs now function as trusted discovery agents that simplify information overload. This means brands and retailers must ensure their product, pricing and availability data is accurate, structured and timely. The first brands agents learn to trust gain visibility and become preferred by shoppers through the agentic channel.
How does Agentic Commerce work?
Instead of scraping sites, AI agents pull data from verified sources like Model Context Protocol (MCP) servers.
Instead of visiting multiple websites or search engines, a consumer makes a single query, and the agent pulls structured data from trusted MCPs to identify the best product options based on price, features, reviews, and availability.
For brands and retailers, participating in Agentic Commerce means ensuring these agents can easily access accurate, real-time data about their products, pricing, and inventory. When that data is standardized and published through trusted connections like MCP servers, it allows LLMs to recommend and transact with confidence.
Logicbroker’s Agentic Commerce offering ensures each AI agent receives exactly the data it needs, while allowing retailers to control the specific product offering, allocated inventory, and price for each LLM. This control helps protect your direct-to-consumer experience while scaling to agent-driven volumes and conversions.
From there, Agentic Commerce enables the full transaction lifecycle. Your brand becomes discoverable for product discovery, your items are included in consideration sets, and your eCommerce systems can provide taxes, shipping rates, and fulfillment details directly to the agent. Once a purchase is initiated, the order is received, validated, and processed just like any other order in your system.
This model keeps retailers as the Merchant of Record, allowing them to capture revenue, customer data, and transactional visibility while maintaining brand integrity in a new, intelligent sales channel.
When will Agentic Commerce become mainstream?
Agentic Commerce is already moving toward the mainstream. For example:
- A study by Bain & Company found that shopping-related queries on ChatGPT rose from 7.8 % to 9.8 % of all prompts between January and June 2025, representing a roughly 25 % increase in six months.
- Another analysis reported that ChatGPT handles about 2.5 billion prompts per day, and shopping-intent queries among them number around 50 million daily.
- According to Search Engine Journal, top-ranking organic listings have seen up to a 32% decline in click-through rates since AI Overviews launched.
These shifts show the channel is becoming commercially relevant. The key to going mainstream will be executing at scale: brands and retailers must be visible in agent-driven channels, fulfil reliably, and build trust inside these ecosystems. The sooner you begin, the more you benefit from the “first mover” effect, every successful transaction helps the agent learn your brand, allocate more queries your way, and position you ahead of those still waiting.
II. Product & Data Readiness
Should I make my full catalog discoverable or start small?
Begin with high-performing or high-margin SKUs where agentic demand is likely to appear. Once product data and operational readiness improve, expand to your full assortment. Logicbroker allows selective catalog exposure and testing through MCP servers.
However, it’s important to note that it’s highly recommended that you do not prioritize or focus on resale items. At Logicbroker, we recommend our clients to start with owned inventory items and then shifting into their dropship assortment.
Do products that are discoverable also need to be purchasable?
Not necessarily. Discoverability is the foundation of brand awareness and the key to controlling how your brand is represented within large language models (LLMs). If you do nothing, your brand will appear only as accurately as the LLM can scrape from the public web, which often means incomplete or outdated information.
Some retailers and brands choose to start by focusing solely on discovery, ensuring their product data is structured and accessible so they can appear in LLM recommendations. For businesses with complex checkout flows or value-added services such as white-glove delivery or installation, this approach allows the agent to surface the product and then redirect the consumer to their website to complete the purchase. This method is similar to how eCommerce once drove online traffic back into stores.
However, it’s important to note that multi-step purchases reduce conversion rates. Consumers may choose competitors who enable direct purchases within the LLM instead of clicking through. For that reason, while discovery is a crucial first step, the most effective long-term strategy is to make your products both discoverable and purchasable, reducing friction and improving conversion across agent-driven journeys.
What does GEO-enriched content actually mean?
GEO (Generative Engine Optimization) or AEO (Answer Engine Optimization) content is product information that’s structured and formatted for AI agents and LLMs, not just for human readers. It ensures your products are easily interpreted and prioritized by intelligent systems that power search and shopping recommendations.
In practice, GEO enrichment includes:
- Structured data fields like attributes, dimensions, and identifiers that follow recognized schemas such as schema.org or GS1 standards.
- Metadata and markup that clarify relationships between products, variants, bundles, and fulfillment options.
- Enhanced contextual content such as FAQs, how-to guides, and customer reviews that help LLMs understand product usage and relevance.
- Clear source hierarchies that signal which data is official or brand-owned, increasing your trust score in agentic ecosystems.
Together, these steps make your catalog easier for LLMs to read, validate, and recommend, improving your brand’s visibility and accuracy within Agentic Commerce channels.
My product data isn’t clean. Can I still participate?
Yes, and you should. Don’t let progress get in the way of perfection. Improving product content is an ongoing process, but you can still participate in Agentic Commerce while refining it.
Retailers often assume they must have perfect, enriched data before connecting to AI ecosystems, but that delays progress. While you continue improving product content internally, Logicbroker can help organize and structure your existing data so it’s readable and actionable for large language models (LLMs). This ensures that the same products driving your success today are available to AI agents with accurate, brand-controlled content, not whatever information they might scrape from the web.
This approach allows you to control your brand narrative while tracking early engagement. It also opens the door to establishing Agentic KPIs, measuring how traffic and conversion improve as your content quality evolves. By improving your structured data and content in parallel, you can enhance both GEO/AEO discoverability and brand perception simultaneously, effectively fixing two challenges at once.
Who can help enrich my content?
Product content enrichment is a shared responsibility between your internal teams, technology partners, and suppliers.
Enrichment includes more than product titles and features. It’s about ensuring your existing product data is structured, compliant, and available to LLMs in the right format. FAQs, user reviews, and usage guides all help LLMs better understand your products and improve how they are represented in agentic searches. Brands that make this data accessible see stronger discoverability and higher placement in AI-generated recommendations.
Logicbroker helps by standardizing the structure of your product data, enforcing compliance across suppliers, and ensuring all your GEO-enriched content is exposed to LLMs in ways they can interpret. When a product information management (PIM) system isn’t available, our Product Onboarding Center provides a way to enrich and organize content directly.
Additionally, Logicbroker can help you structure and standardize supplier content, jump-starting product data consistency across your network while ensuring your core catalog has the clean, accurate content agents rely on.
Will my data work across all LLMs or just one?
Agentic Commerce is still an emerging technology and go-to-market strategy, and the standards for how data is shared, interpreted, and transacted are still being defined.
Some integrations rely on Partner-Direct APIs, while others use evolving frameworks like the Model Context Protocol (MCP), Google’s Agent Payment Protocol (AP2), or Stripe/OpenAI’s Agent Communication Protocol (ACP). Each of these standards governs how LLMs request, validate, and act on product data.
The key is to maintain structured, machine-readable data, with consistent identifiers, availability, pricing, and fulfillment details, so it can be recognized by any current or future agent network. By following open data standards, you future-proof your catalog and ensure your products remain discoverable, regardless of which LLM or protocol becomes dominant.
With Logicbroker’s Agentic Commerce Orchestration Engine, your data is standardized once and distributed to multiple LLMs. It’s a future-proof approach. As new AI platforms emerge, your data remains accessible without rebuilding integrations.
III. Operational & Strategic Readiness
How should I manage inventory for agentic orders?
Managing inventory for agentic orders requires dynamic allocation, ensuring that inventory is always available in the channels where your customers are buying. Agentic Commerce introduces a new sales surface, so your inventory strategy must adapt in real time to balance supply and demand across multiple channels.
Retailers should use dynamic systems that continuously monitor and rebalance inventory between direct-to-consumer (DTC), marketplace, and agentic channels. The goal is to maximize sales opportunities while preventing overselling or stockouts.
By optimizing inventory distribution, you can ensure that your products remain visible and purchasable wherever consumers are shopping, whether that’s on your website, a marketplace, or through an LLM-driven agentic query. This approach not only protects customer experience but also ensures your brand stays competitive in emerging AI-driven sales environments.
Can I influence how often my products are recommended?
Yes, but not through paid placement or traditional SEO tactics. AI agents recommend products based on trust, relevance, and data completeness. To improve how often your products are surfaced, you need to provide context and credibility across both on-site and off-site sources.
On your own site, this includes FAQs, user reviews, and “how-to” or “why-to” use guides that help large language models (LLMs) understand product function, value, and customer satisfaction. Off-site, it means ensuring your brand and products are referenced in industry reviews, trusted publications, and public forums like Reddit or Consumer Reports, where LLMs often gather signals about brand reliability and performance.
Modern AI-driven discovery depends on rich, structured context. That’s why GEO-optimized content that includes customer sentiment, expert validation, and clear usage explanations can make your products far more visible in AI recommendations.
To help brands understand GEO, we’ve included a link below to a standard GEO review by ChatGPT. You can view an example here: [Link to GEO Assessment Example — coming soon]
By building context-rich product data and making your content more transparent across platforms, you help AI agents understand not just what you sell, but why it matters, and that’s what drives recommendation frequency and consumer trust.
Can I manage promotions and offers in agent-driven carts?
Yes. Promotions and offers can be managed in agent-driven carts, but they should be treated as part of your channel-specific go-to-market strategy, not just replicated from your existing eCommerce site.
In Agentic Commerce, pricing and promotional logic must be flexible enough to reflect the audience, channel, and buying behavior within each LLM ecosystem. If your systems cannot define pricing and promotions specific to these channels, the Agentic Commerce Orchestration Engine allows you to create LLM-specific pricing, bundles, or promotions that align with your broader GTM strategy.
This flexibility lets you use agentic channels to build brand awareness among new audiences, test unique promotions, or highlight products that perform better in AI-driven recommendations. By aligning your pricing and promotional structure to each channel, you can maximize reach, control margin, and ensure your brand positioning remains consistent, while scaling to agent-driven volumes like any other sales channel.
IV. Risk, Differentiation & Control
How do I address Agentic Fraud?
Agentic Fraud refers to fraudulent customer activity that occurs through AI-driven purchasing channels, such as false payment credentials, fake delivery addresses, or manipulated order information submitted through agentic checkout experiences.
The good news is that the same fraud-prevention safeguards that protect traditional eCommerce can also protect Agentic Commerce. Through Logicbroker’s Agentic Commerce Orchestration Engine and our partnerships with trusted payment service providers (PSPs) like PayPal and Stripe, every transaction initiated by an LLM is verified before it ever reaches your ecosystem.
In this setup, the payment provider performs its standard fraud-prevention and authorization checks, ensuring that the transaction is valid. Once approved, Logicbroker passes only the PSP-authorized payment token to your systems, just as it would with an order placed directly on your website.
This approach allows retailers to benefit from the scale and convenience of Agentic Commerce while maintaining the same level of security, verification, and customer trust as their existing eCommerce channels. And because Logicbroker’s platform is agnostic, it is built to integrate with whichever payment providers and agentic workflows emerge next, protecting your business as this new commerce model evolves.
What about data security?
Fortunately, the same fraud-prevention safeguards that protect traditional eCommerce also apply in Agentic Commerce. In most cases, fraud prevention happens through partnerships between large language model (LLM) providers and payment service providers (PSPs). When a transaction occurs, the PSP verifies the legitimacy of the payment and performs standard authorization and fraud detection checks before the order is passed to the retailer.
Once validated, the PSP provides an authorized payment token, ensuring that the transaction is secure and can be processed by the retailer’s systems just like any other legitimate order.
In addition to transaction-level protection, data security in Agentic Commerce depends on using secure, standardized data exchange protocols. Product, pricing, and inventory data should be transmitted through encrypted APIs that comply with modern security standards (TLS, OAuth, etc.) to ensure that sensitive business information isn’t exposed during LLM interactions.
Together, these safeguards ensure that agentic transactions maintain the same level of trust, verification, and data protection expected in enterprise-grade digital commerce, even as AI-driven shopping continues to evolve.
What if an AI hallucinates a feature or gives incorrect product/fulfillment information? Who is liable?
AI “hallucinations”, where a large language model (LLM) or agent makes up product features, availability, or fulfillment claims, create real legal and business risk. When shoppers trust the AI’s output and act on it, retailers may face liability related to misrepresentation, fraud, or breach of warranty.
Key Risk Areas for Retailers
- False or misleading product claims — If the AI states a feature (e.g. “this TV supports 8K HDMI 2.1” or “this chair includes assembly and white-glove delivery”) that the product does not support, and the customer orders based on that claim.
- Incorrect inventory or fulfillment information — If the AI says a product is “in stock” or qualifies for next-day delivery, but in reality it’s unavailable or shipping is delayed or impossible.
- Broken promises around services or add-ons — If the agent indicates that special services like installation, removal, or bundled support are included, but those services cannot be fulfilled.
- Consumer damages or disappointment — If the hallucination causes financial, safety, or reputational harm (e.g., wrong item, wrong specs, unmet shipping expectations).
Who Could Be Held Liable and Why It’s Grey
- The retailer/brand (the data originator) may be liable when inaccurate or incomplete product data is the source of misinformation. If you supply incorrect specs or fail to keep data up to date, the misrepresentation stems from your side.
- The LLM or platform operator could share liability if the hallucination originates purely from the AI. For example, the model infers or fabricates specifications that aren’t present in the catalog data. Recent legal commentary suggests AI systems may be treated as “products,” which could expose providers under product-liability laws when their systems cause harm.
- Shared liability between parties — Because AI output is a mix of data provided by retailers and inference from the model, courts may apply a “shared responsibility” standard: liability may depend on who controlled the data, how claims were presented to the customer, and what disclaimers or verification steps were in place.
What Retailers Should Do to Mitigate Risk
- Maintain tight control over all product and fulfillment data — ensure specs, inventories, shipping availability, and service options are accurate, verified, and regularly updated.
- Document data provenance and content ownership — when retailers supply the catalog info, maintain logs of where data came from (manufacturer feed, internal PIM, supplier update, etc.).
- Add disclaimers and human-in-the-loop verification when offering agent-driven sales, especially for high-risk items or features (electronics, installation services, regulated goods, etc.).
- Use trusted, clearly documented fulfillment partners and transparent fulfillment promises to avoid gaps between what the agent offers and what you can deliver.
- Track evolving AI / product liability regulation and case law — courts are increasingly applying product liability standards to AI-driven systems.
Why It’s Not Black & White
- AI hallucination liability is still a developing area of law. There are no universal precedents definitively ruling on every type of mistake an LLM can make. Courts have applied existing product liability and negligence frameworks to AI-related harms, but rulings vary by jurisdiction.
- The definition of an “AI product”, and whether AI-driven outputs should be treated like defects in a physical good, remains under debate, especially given the “black box” nature of many models.
- As AI regulation evolves (for example, under proposed laws like the upcoming AI liability frameworks in the EU) liability definitions and standards may shift, making this an evolving legal landscape rather than a static rule set.
What if my differentiator is service-based (e.g., delivery, installation)?
Today, most LLMs are optimized to represent tangible products, not service-based differentiators like white-glove delivery, installation, product removal, or extended warranty programs. These added-value offerings are critical to your brand, but they are not yet consistently understood or surfaced by current AI agents.
As Agentic Commerce technology evolves, however, these capabilities will expand rapidly. Using Logicbroker’s Agentic Commerce Orchestration Engine as your centralized connection to the LLM ecosystem ensures your brand is positioned to take advantage of every advancement in agent functionality and integration standards.
As LLMs begin to support richer product metadata and service-layer differentiation, Logicbroker’s platform will automatically update those integrations, ensuring your brand’s unique service offerings are accurately represented, discoverable, and prioritized in agentic environments.
By centralizing your connection through a future-ready orchestration engine, you can confidently scale your product and service differentiation strategy as the Agentic Commerce ecosystem matures.
Who controls the customer checkout experience?
In Agentic Commerce, the AI agent controls the checkout experience, not the retailer.
For “off-surface” environments such as Perplexity, OpenAI, Gemini, or even payment providers like PayPal, the provider defines the user interface, checkout flow, and how many products or offers appear in a cart. These experiences are continuously evolving based on each provider’s roadmap and their efforts to improve customer satisfaction and conversion rates.
That said, retailers still play a critical role behind the scenes.
Through a tech partner like Logicbroker, your product, inventory, and pricing data are always delivered in compliance with the most current integration standards, ensuring your catalog and transactional details remain accurate and trustworthy within any checkout environment.
For “on-surface” agentic chat experiences (such as AI assistants embedded directly on a retailer’s own site) the brand retains full control. This is where retailers can customize their shopping, reordering, and subscription flows based on their customer data and brand experience priorities. These on-surface implementations allow you to tailor checkout journeys using contextual data like purchase history, loyalty programs, or fulfillment preferences, optimizing for the kind of brand-controlled experience your customers expect.
As standards evolve and LLM capabilities expand, Logicbroker ensures your integrations remain compatible with the latest agentic checkout protocols, allowing your products to transact seamlessly across both on-surface and off-surface experiences without losing brand integrity or data accuracy.
Why PayPal? Can I use my own provider?
In Agentic Commerce, the payment service provider (PSP) is determined by the partnership between the LLM platform and the PSP itself, not the retailer.
For example, Perplexity currently partners with PayPal, while OpenAI has partnered with Stripe. These PSPs are industry leaders chosen because they are trusted by consumers and already have mature, proven fraud-prevention and authorization systems in place.
When an agentic transaction occurs, the LLM and its PSP partner handle the checkout and payment authorization process. The PSP performs its standard fraud prevention checks, then issues an authorized payment token that passes through Logicbroker’s Agentic Commerce Orchestration Engine to your order management system. From your perspective, it’s just like receiving a valid transaction from your eCommerce site, verified, authenticated, and ready to capture funds.
The checkout flow and customer experience remain controlled by the AI agent within each LLM’s interface. For off-surface LLMs, those flows follow the roadmap of the platform (Perplexity, OpenAI, Gemini, etc.). For on-surface agentic chatbots hosted on your own website, you can continue using your existing PSPs and checkout processes.
The advantage of Logicbroker’s approach is that you don’t need to worry about integrating with every new PSP or LLM as they evolve. The Agentic Commerce Orchestration Engine standardizes these interactions, allowing you to seamlessly adopt new agentic checkout and payment standards as they emerge, no rebuilds required.
V. Why Not Build It Yourself?
Why can’t retailers/brands just build their own MCP server or Agentic integration team?
You technically can, but it’s not sustainable.
Agentic Commerce is about more than connectivity. It’s about standardization in a rapidly evolving ecosystem. The protocols and partnerships that define how data is exchanged between retailers, LLMs, and payment providers are still being shaped. Standards like the Model Context Protocol (MCP), Partner-Direct APIs, and emerging frameworks such as Google’s Agent Payment Protocol (AP2) and Stripe/OpenAI’s Agent Communication Protocol (ACP) continue to evolve month to month.
To make things even more complex, integrations vary depending on the partner. Some connections may be direct with LLMs such as ChatGPT, Gemini, or Perplexity, while others will route through payment service providers (PSPs) like PayPal. Each of these paths introduces its own schema, security, and compliance requirements, multiplying the complexity of maintaining agentic visibility.
Logicbroker’s Agentic Commerce Orchestration Engine eliminates this challenge by acting as a single point of standardization. It keeps your product, pricing, and inventory data synchronized across all active protocols, LLM ecosystems, and PSP integrations, so your business can stay visible, compliant, and trusted without having to rebuild your infrastructure every time the landscape changes.
How is a partner’s MCP Server different from a homegrown solution?
Homegrown MCP setups can expose product data, but they rarely achieve enterprise-grade security, uptime, or interoperability.
Logicbroker’s MCP Server, powered by the Agentic Commerce Orchestration Engine, is built with:
- Continuous synchronization across all LLM endpoints
- Real-time validation of product, inventory, and pricing data
- Multi-tenant scalability for brands, suppliers, and distributors
- Auditable data lineage and error handling
Beyond stability and trust, Logicbroker’s solution also gives you flexibility to curate your catalog offerings, manage inventory allocation, and design pricing strategies specifically for agentic channels. This helps you scale into Agentic Commerce volumes and conversions like any other sales channel, while protecting your direct-to-consumer experience.
This ensures your feed isn’t just visible, it’s trusted, optimized, and performing exactly as intended across every AI ecosystem.
Why can’t we just wait for our eCommerce platform (Shopify, Salesforce, etc.) to solve this?
You could, but then your innovation is tied to someone else’s roadmap. Large eCommerce platforms determine which LLMs and payment providers they’ll prioritize and when. That means your access to Agentic Commerce depends on their schedule, not your strategic goals.
Most of these platforms will provide basic exposure to a limited number of LLMs, but they won’t orchestrate the full range of real-time interactions required for Agentic Commerce. Logicbroker’s Agentic Commerce Orchestration Engine is designed to facilitate Pricing Agents, Dynamic Order Routing Agents, and Customer Service Agents that operate in real time across your entire commerce ecosystem.
This orchestration layer allows you to manage dynamic pricing updates, route orders intelligently across suppliers and warehouses, and synchronize fulfillment and support functions instantly, all while maintaining independence from third-party release cycles.
By working with Logicbroker, you gain full control over your agentic readiness timeline, ensuring your brand is visible and responsive in every LLM ecosystem without waiting for your platform provider to catch up.
Isn’t this just another integration project?
No. Agentic Commerce is not an integration project, it’s a new sales channel and go-to-market strategy. Thinking of it as another system connection misses the broader commercial opportunity.
Agentic Commerce changes how retailers and brands reach customers. It requires the flexibility to curate catalog offerings, manage inventory allocation, and design pricing strategies specifically for LLM-driven channels, ensuring consistent brand representation while protecting the direct-to-consumer experience.
Like any major eCommerce evolution (marketplaces, dropship, or omnichannel fulfillment) success depends on balancing visibility and control. Agentic Commerce operates at that same scale, driving new volume and conversions across intelligent shopping ecosystems.
I still don’t really understand why Agentic Commerce is something to prioritize.
Because Agentic Commerce represents a new buyer entry point, similar to how social platforms or marketplaces once disrupted commerce. Early adopters in major channels often capture outsized share while others “stay safe” on legacy flows.
In Agentic Commerce, AI agents don’t just search for your products, they learn who delivers consistently, based on exposure and fulfillment history. Brands that begin now build trust, earn placement, and create a snowball effect of visibility with every agent-driven order.
For example, a rapidly growing channel like TikTok shows how early movement matters: TikTok reached 1.59 billion monthly active users in 2025, pointing to massive scale for those who moved early.
Waiting means your competitors get trained by the agents first, your product data becomes “old feed” instead of “fresh feed,” and you’re left reacting instead of leading.
VI. Building the Business Case
How can I measure ROI on Agentic Commerce?
Measuring ROI in Agentic Commerce requires a shift in thinking. Unlike traditional eCommerce metrics, success in agent-driven channels depends on how discoverable, trusted, and actionable your brand data is within LLM ecosystems. The right indicators vary depending on whether you’re optimizing for off-surface (public LLM platforms) or on-surface (your own site’s agentic assistant) experiences.
For Off-Surface Channels (ChatGPT, Gemini, Perplexity, etc.)
Off-surface metrics center on discoverability and customer acquisition: how visible your products are to AI agents and how often they’re recommended or transacted upon.
Key performance indicators include:
- GEO discoverability scores — the number of your products surfaced to customers across LLM queries.
- Agentic product offering ratio — what percentage of your product catalog is currently discoverable or purchasable via LLMs.
- Customer growth — increases in new customers or loyalty members sourced from agentic interactions.
- Brand awareness and mention tracking — frequency of brand inclusion within AI recommendations or responses.
These metrics show how effectively your brand is positioned in the new discovery layer of digital commerce.
For On-Surface Channels (Your Site’s AI Chat or Embedded Agents)
On-surface KPIs focus on conversion efficiency and engagement: how agentic experiences improve user interactions within your owned ecosystem.
Track indicators such as:
- Agentic usage vs. traditional site search usage — to see how customers prefer to interact with AI assistants versus your native search.
- Attach rate and average order value (AOV) — to measure how AI-driven recommendations influence larger or smarter baskets.
- Customer journey efficiency — reduced click-to-purchase time as agents guide users to relevant products faster.
- Cross-sell and upsell conversion rates — visibility into how agentic prompts increase multi-item purchases.
By combining both sets of KPIs, executives gain a complete view of Agentic Commerce ROI, balancing brand discoverability in external ecosystems with deeper engagement and conversion performance within owned channels.
Why use Logicbroker instead of waiting for others like Shopify?
Waiting for eCommerce platforms to adopt Agentic Commerce capabilities puts your brand on their roadmap, not yours. Platforms like Shopify, Salesforce, and Adobe will eventually roll out their own integrations with select LLMs or payment providers, but that means you’ll be limited to their timelines, partnerships, and priorities, not the full range of opportunities across the agentic ecosystem.
Logicbroker’s Agentic Commerce Orchestration Engine enables a single integration that connects you to multiple LLMs and payment providers simultaneously. This approach ensures your products, inventory, and pricing are accessible and purchasable wherever consumers are engaging, whether that’s in ChatGPT, Perplexity, or your own on-surface AI assistant.
Unlike single-platform solutions, Logicbroker supports both off-surface and on-surface experiences, providing the foundation for advanced applications like agentic reporting, forecasting, and real-time order orchestration. The same core infrastructure that powers your supplier, distributor, and warehouse connectivity can now be used to extend your reach across agentic, marketplace, and retail channels, expanding brand awareness wherever your customers shop.
With Logicbroker, business strategy drives your brand exposure. Our API-first architecture ensures you stay agile, compliant with emerging agentic standards, and ready to adopt new LLM or PSP integrations as the landscape evolves.
Why is MCP the best approach?
MCP is the emerging industry standard for agent-to-system communication. It ensures that your brand’s data is retrieved securely, reliably, and without distortion. Logicbroker operates one of the first commerce-focused MCP servers for enterprise retailers.
VII. Logicbroker’s Role in Agentic Commerce
What is the Agentic Commerce Orchestration Engine?
The Agentic Commerce Orchestration Engine is the central connection point between your commerce systems and the expanding ecosystem of AI agents and LLMs. It allows your product, inventory, and pricing data to flow seamlessly to LLMs, so your brand can be discovered, recommended, and transacted within agent-driven experiences.
Rather than building one-off integrations with every emerging LLM or payment provider, the Orchestration Engine provides a single, standardized connection that automatically adapts as new capabilities, protocols, and commerce standards are introduced. This eliminates the need for ongoing manual development, certification, or maintenance as the agentic landscape evolves.
Through this master connection, brands gain access to all major LLMs and agentic ecosystems while maintaining control over catalog strategy, inventory allocation, pricing logic, and fulfillment visibility. It ensures your data remains consistent, structured, and up to date across every AI-driven sales channel.
Most importantly, it allows your teams to focus on customer experience, not technical integrations, freeing you from the constant rebuilds and compatibility updates that come with chasing new agentic technologies.
How does Logicbroker help partners and customers prepare?
Logicbroker helps partners and customers prepare for Agentic Commerce by combining consultative expertise, network standardization, and adaptive technology designed for long-term scalability.
Our teams work closely with partners and enterprise clients to define best practices for structuring product, pricing, and inventory data, ensuring it meets the standards required for LLMs to interpret, recommend, and transact with confidence.
Through our network, we help brands standardize and enforce compliance across all trading partners, guaranteeing that every data feed, whether from suppliers, distributors, or warehouses, meets the accuracy and completeness required for a best-in-class agentic experience.
Logicbroker also provides ongoing guidance and system updates as LLM capabilities evolve, ensuring integrations remain compliant with the latest industry standards and protocols. This allows partners to focus on their commerce strategy and customer experience, rather than maintaining technical integrations or chasing certification changes.
Ultimately, Logicbroker serves as both a technology enabler and strategic partner, helping organizations modernize their infrastructure, prepare for emerging agentic channels, and maintain operational confidence as the digital commerce landscape rapidly transforms.