From Advice to Action: Scaling the Project-to-Done Agentic Workflow Across Retail
Retail leadership is moving beyond simple LLM-based conversational tools, focusing instead on implementing autonomous agentic systems designed to manage intricate, multi-stage processes with minimal human intervention.
This “project-to-done” workflow represents a fundamental restructuring of commerce, in which the traditional multi-step shopping journey (discovery, comparison, selection, and transaction) compresses into a single, fluid interaction mediated by intelligent agents.* Scaling these agentic workflows is now the critical factor distinguishing market leaders from those hampered by legacy inefficiencies, particularly as the retail sector grapples with heightened margin pressures and swiftly evolving consumer habits.
2026 Retail Market Overview in the Agentic Era
Retail faces a digital paradox: despite record-breaking tech investments, achieving measurable ROI remains elusive for most organizations.
McKinsey’s 2026 research on the European grocery sector highlights that, while renewed momentum is emerging, margins remain under persistent pressure, forcing retailers to seek growth through private labels, adjacencies, and the aggressive application of AI to core functions. This environment has birthed the agentic-AI-empowered merchant, an individual who spends less time on manual reporting and more time on high-level strategy, as autonomous systems handle routine merchandising decisions at scale.
Deloitte’s 2026 Retail Industry Global Outlook further clarifies this shift, noting that nine in ten retail executives expect AI to surpass traditional search engines as the primary tool for product discovery by 2026. Perhaps more significantly, half of these leaders expect the multi-step shopping journey to collapse entirely by 2027 as autonomous agents begin acting on behalf of consumers to perform search, comparison, and recommendation tasks. This extends into physical retail, where “agentic stores” embed intelligence directly into the shopping environment, connecting digital engagement with in-store sensors and real-time inventory systems to create a hyper-personalized ecosystem.
AI Maturity in the Retail Industry
The acceleration in the adoption and impact of agentic AI across the global retail sector is dramatic.* However, organizational structures are struggling to keep pace.
While AI is delivering significant gains in productivity and efficiency, only 34% of companies are truly reimagining their business models around AI, with the majority still focusing on surface-level automation or redesigning isolated processes.*
The challenge is to move beyond these silos and implement a unified architectural approach that treats AI agents as a “global digital taskforce” capable of proactively anticipating goals and reasoning through complexity.
Architectural Pillars: Standardizing the Agentic Workflow
To move from “advice” (generative AI) to “action” (agentic AI), a standardized set of protocols must be in place to ensure that agents can securely interact with enterprise systems, manage payment flows, and communicate with other agents.
Google Cloud has been at the forefront of developing these open standards, ensuring that the agentic economy remains interoperable and secure.
The three core pillars of this architecture are:
- Model Context Protocol (MCP)
- Universal Commerce Protocol (UCP)
- Agent Payments Protocol (AP2)
1. Model Context Protocol (MCP): The Universal Connector for Enterprise Systems
The Model Context Protocol (MCP) serves as the standardized interface that enables AI agents to interact with a wide range of enterprise retail systems, from legacy inventory management systems to modern customer service platforms. Described as the “USB-C for AI,” MCP provides a unified way for applications to deliver rich context to LLMs without the need for custom, brittle API integrations across different platforms.
In a retail environment where data is often trapped behind departmental walls or within isolated legacy systems, MCP allows agents to query servers dynamically to understand available capabilities, request permissions, and execute secure actions.
For enterprise-scale retail, the security implications of MCP are paramount. Traditional API integrations often require hardcoding connections or storing API keys in plaintext, creating significant vulnerabilities. In contrast, MCP runtimes encrypt tokens at rest and never expose credentials directly to the language models. Furthermore, MCP supports a multi-user authorization model, allowing agents to act securely on behalf of specific employees or customers without granting broad, over-privileged access to the entire system.
2. Universal Commerce Protocol (UCP): Standardizing the Buying Loop
While MCP handles the internal enterprise connection, the Universal Commerce Protocol (UCP) standardizes the external commerce journey. Developed by Google in collaboration with industry giants such as Shopify, Etsy, Wayfair, Target, and Walmart, UCP is an open-source standard designed to enable seamless, agentic commerce actions across consumer surfaces like AI Mode in Google Search and the Gemini app.*
UCP provides a common language and functional primitives that enable AI agents to navigate the entire shopping journey, from initial product discovery and pricing to checkout and post-purchase support, without requiring bespoke integrations for each retailer.
The technical architecture of UCP is built on “Capabilities” (primary functions, such as cart management) and “Extensions” (specialized logic, such as applying discounts). A business exposes these capabilities via a standardized JSON manifest located at /.well-known/ucp endpoint, allowing agents to discover supported services and payment options autonomously. This modularity ensures that merchants remain the “Merchant of Record,” retaining full ownership of customer relationships and data while their products are transacted across the broader agentic ecosystem.
3. Agent Payments Protocol (AP2): Engineering Trust in Transactions
The final architectural hurdle for autonomous commerce is the verification of intent in financial transactions.
Standard payment systems assume a human is present to click “buy” on a trusted interface. Agentic commerce breaks this assumption, necessitating the Agent Payments Protocol (AP2). AP2 is an open, non-proprietary protocol that establishes a secure and auditable framework for payments initiated by AI agents, extending the foundations of MCP and A2A.
AP2 builds trust through a system of “Mandates”, tamper-proof, cryptographically signed digital contracts that serve as verifiable proof of a user’s instructions.* The protocol chains three types of mandates:
- Intent Mandate: Captures the initial request and constraints (e.g., “Buy shoes under €100”).
- Cart Mandate: Provides explicit authorization for a specific set of items and prices.
- Payment Mandate: Communicated to the payment network to signal agent involvement and user presence.
The implementation of AP2 dramatically reduces the risk of fraud in autonomous systems. By shifting from probabilistic AI inferences to deterministic, signed proofs, AP2 can reduce tampering rates in traditional API-centric models. The mathematical foundation of this trust is ECDSA signatures and verifiable credentials, ensuring non-repudiable accountability.
Sector-Specific Autonomous Workflows: Use Cases for 2026The scaling of agentic AI is not a one-size-fits-all endeavor. The requirements of a high-frequency grocery environment differ from those of the high-touch world of electronics. Google Cloud’s Gemini Enterprise Agent Platform helps retailers build grounded agentic systems that move from providing advice to completing entire projects. Grocery: The “Kitchen-to-Table” AgentInstead of just suggesting a recipe, this agent acts as an autonomous meal planner and logistics manager. The Action: The agent analyzes health goals, pantry inventory, and weekly schedules to generate a meal plan. Autonomous Task Execution Workflow:
 Fashion: The “Look-to-Launch” AgentMoving beyond “Virtual Try-On” to act as a personal wardrobe curator and event stylist. The Action: A user describes an upcoming event (e.g., “a beach wedding in Italy”). The agent cross-references the user’s current wardrobe with new arrivals and executes tailoring requests. Autonomous Task Execution Workflow:
 Consumer Electronics: The “Specs-to-Setup” AgentTransitioning from product comparison to full lifecycle technical support. The Action: The agent manages the selection of a complex ecosystem, identifying compatible soundbars, mounts, and cables, and automatically books a verified installer. Autonomous Task Execution Workflow:
 Cosmetics: The “Mirror-to-Maintain” AgentIn 2026, wellness is firmly embedded in beauty routines, with 82% of consumers seeking personalized solutions.* The agent acts as a holistic skincare and treatment manager. The Action: The agent moves from static skincare advice to active routine orchestration and professional service management. Autonomous Task Execution Workflow:
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The Blueprint for Success in Agentic Retail
The transition to agentic retail is already evident in forward-thinking organizations. Their success is built on a foundation of clean data, executive sponsorship, and a willingness to move beyond isolated pilots.
The Home Depot: Pioneering the “Project-to-Done” Workflow*
The Home Depot has set a global benchmark for agentic commerce by launching agents that move beyond advice to take direct action. Utilizing the Gemini Enterprise for CX platform, these agents assist both DIYers and professionals in bringing complex projects from “How-to” to “Done.”
- Complex Execution: The agents manage complex material orders and provide specific, technical recommendations tailored to the user’s project requirements.
- Standardized Buying: By endorsing the Universal Commerce Protocol (UCP), The Home Depot enables these agents to navigate product discovery, inventory verification, and secure checkout within a single interaction.
- Operational Impact: This model collapses the traditional search and discovery process, allowing associates and customers to focus on execution rather than logistics.
Zalando: A Vision of Pan-European Agentic Commerce*
Zalando’s success stems from its “AI in our DNA” philosophy, which has built its data foundations over the past 15 years. Zalando has accelerated its progress through:
- The Zalando Assistant: A shoppable lifestyle companion that has interacted with over 6 million customers.
- UCP Endorsement: By pioneering agentic commerce through the UCP, Zalando has enabled seamless AI-powered checkout and frictionless one-tap buying on surfaces beyond its own apps.*
- Compounding Efficiency: Through AI-accelerated development, Zalando has seen a 22-percentage-point increase in exact-day delivery promises and ships 20% more software code changes YoY.*
Carrefour: Transitioning to “Searchless” Retail
Carrefour’s 2030 Strategic Plan identifies AI, Tech, and Data as a primary springboard for performance and personalization. By adopting Google’s UCP, Carrefour has anticipated the transition to a “searchless” retail model where discovery is mediated by AI agents.* The integration of the Hopla agent has already begun to facilitate the creation of shopping carts, allowing Carrefour to win the battle for price competitiveness and customer delight in a high-inflation environment.
Scaling from Pilot to Production
Scaling the “project-to-done” agentic workflow requires a strategic partner capable of turning technical complexity into operational clarity. As a Google Cloud Premier Partner, Kartaca brings 15+ years of experience in cloud migration, data analytics, and the development of specialized retail technologies.
Integrated Service Offerings for Retail Leaders
We provide a comprehensive suite of services designed to address the specific pain points of retailers:
- Cloud Native Retail Solution Package: Combines advanced e-commerce technologies with cloud scalability to optimize operations and elevate customer experiences.
- Lixus Loyalty Solutions: Enables personalized, behavior-based rewarding and cross-channel marketing at scale.
- Data Lake & Warehouse Modernization: Transforms fragmented data into a unified intelligence engine, enabling real-time analytics and grounded AI.
- Identity & Security Expertise: Implementing Google Cloud Identity & Security solutions to ensure GDPR compliance and protect proprietary data context.
From Strategic Advice to Autonomous Action
The era of the agentic enterprise is the current reality of the global retail market. Kartaca’s momentum as a global next-gen technology provider is built on a rigorous, detail-driven approach that helps organizations turn complexity into clarity.
Whether you are a “cloud native” challenger or a legacy leader looking to reinvent your core processes, the path to agentic retail starts with a strategic partner who understands modern commerce.
Take the Next Step Toward Autonomous Commerce.
Is your organization ready to transition from AI pilots to scaled agentic workflows? Contact us today to discover how we can help you build the “project-to-done” retail ecosystem of tomorrow. We are ready to lead your digital transformation.
Author: Gizem Terzi Türkoğlu
Published on: Jun 30, 2026
