SAP and Google Cloud are introducing agent commerce architecture to automate multi-agent marketing and retail operations at enterprise scale.
According to a study by SAP, 78% of businesses believe AI will be critical to customer retention in 2026. However, the same data reveals that fewer than two in five companies share customer data across customer experience (37%) or CRM (39%) platforms.
Addressing this structural data failure requires direct infrastructure intervention. SAP and Google Cloud have expanded their partnership to create an agent customer experience architecture that connects data, AI, engagement, and commerce operations.
This deployment relies on reimagining how AI interacts with back-end commercial platforms. Most digital commerce infrastructure relies on fragmented APIs. SAP Commerce Cloud employs the Universal Commerce Protocol to standardize data exchange between retailers, payment gateways, and autonomous agents. This framework allows the software to independently run the entire retail sequence from initial search, transaction processing, to post-sale resolution.
Universal Commerce Protocol Deployment
Engineering teams that integrate universal commerce protocols facilitate direct interaction between intelligent agents and commerce platforms. Standardization reduces integration costs and accelerates onboarding to AI-driven channels.
SAP plans to work with Google to, among other things, incorporate AI mode functionality to ensure merchant products are organically displayed across Gemini applications and Google Search. Consumers interact with these interfaces, and the backend architecture handles inventory checking, cart management, and payment processing without requiring retailers to rebuild their existing infrastructure.
SAP Commerce Cloud integrates the functionality of Google Gemini to power the designated shopping assistant. Brands can bring Assistant directly to consumers to drive engagement through chat, voice, and text. State persistence remains active throughout the shopping cycle. This deployment ingests live behavioral inputs, current warehouse capacity, and active marketing data to assemble personalized product combinations with complete event configurations. By continuously improving the recommendations, the application ensures high relevance and strict physical fulfillment functionality.
Enterprise systems often fail when promotional campaigns create demand that physical inventory cannot meet. Digital purchases are frequently stalled because the front-end interface cannot sync with the back-end warehouse system. Users regularly click on promotional emails, load related mobile applications, and suddenly face out-of-stock notifications during checkout. Fulfillment updates experience significant delays and support agents lose visibility into the complete operational status. SAP and Google Cloud designed a joint solution to fix these specific systemic customer experience failures.
This architecture integrates the entire sequence rather than managing broken contacts. Traditional commercial setups require consumers to repeatedly enter previously shared information. Support staff often do not have access to consolidated records and are unable to resolve issues effectively. This integration targets these operational breakdowns, allowing systems to instantly recognize users and their precise context across all digital assets.
Bidirectional data flow
Marketing execution requires highly accurate data pipelines. SAP Engagement Cloud partners with Google Cloud to develop an autonomous multi-agent framework. The technical foundation relies on SAP Business Data Cloud Connect for Google BigQuery. This deployment relies on bidirectional zero-copy data links protected by strict administrative controls. Leaving vast data stores in place rather than replicating them reduces storage costs and network latency.
BigQuery ingests live variables such as weather conditions, precise location, and active ad interaction rates. SAP Customer Experience solutions provide internal behavioral context and track customer profiles, accurate transaction history, specific service interactions, and agreed engagement records. SAP Engagement Cloud enables unified intelligence and deploys autonomous agents to orchestrate personalized interactions across the customer lifecycle.
While BigQuery handles the logic, it routes the information through Business Data Cloud to force immediate synchronization of inventory. Shopping Assistant actively queries live warehouse records before displaying your products. The software checks physical supplies against consumer demands and verifies availability before making offers.
Generative execution in production
Advanced generative models determine localized output for marketing campaigns. Google Gemini models, especially the Nano Banana 2 iteration, offer specialized agent skills. The model dynamically generates localized messaging, customized images, and campaign variations based on precise specifications provided by bidirectional data flows.
The introduction upgrades standard text messages to an immersive and interactive interface through Google Rich Communication Services. Ad creatives continually evolve based on the engagement data they receive. The system processes the interaction, evaluates the response to the user profile, and instructs the Nano Banana 2 model to adjust subsequent communications.
Marketing departments achieve greater efficiency by abandoning manual execution. Instead of setting strict campaign parameters, teams set business goals and provide enterprise data access to SAP Engagement Cloud. The autonomous agent coordinates the necessary steps, segmenting the audience based on Google BigQuery analytics, and generating specific content variations through Google Gemini models.
Assessing the impact on infrastructure
Implementing this architecture restructures standard commerce operations. Consumers direct their purchase intent to search engines and conversational interfaces. A built-in AI agent processes intents, navigates Universal Commerce Protocol connections, and completes purchases directly against your enterprise backend.
Even if the transaction occurs within a third-party environment, the retailer retains full ownership of the relationship with the customer. This architecture captures consented engagement data and feeds transaction history back to SAP Customer Experience solutions. The system updates the localized customer profile and provides new context to the Google Gemini model ahead of the next engagement cycle.
This system continuously improves campaign performance without requiring direct human intervention. The multi-agent framework evaluates the success of the generated rich communication service text message and adjusts variables before the next automatic dispatch.
See also: How computer vision increases retail productivity
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