What is agentic AI and how does it differ from generative AI in fashion?

Agentic AI operates autonomously to achieve goals through multi-step execution, whilst generative AI produces content in response to prompts. The core distinction matters for fashion businesses now deploying both technologies across operations and customer experience. Generative AI creates text, images or recommendations when asked. Agentic AI monitors data, makes decisions and takes action without waiting for human instruction at each step.

What is the difference between agentic AI and generative AI?

Generative AI creates new content such as text or images, whilst agentic AI takes autonomous actions to achieve a specific goal. The main distinction is that generative AI is reactive and agentic AI is proactive. Agentic AI focuses on autonomous action and goal achievement, and is inherently goal-oriented, whereas generative AI is content-creation oriented.

Agentic AI makes decisions and takes action to keep a process going, whilst generative AI reacts to input and creates output. Agentic AI specialises in workflow automation and independent problem-solving, whilst generative AI's strength is content creation. Generative AI creates content like text, images and code from a prompt, whilst agentic AI plans and executes complex tasks end to end without waiting for instructions. Generative AI makes individuals faster, but agentic AI changes how entire processes run.

Most agentic systems use generative AI, typically large language models, as their reasoning engine. The two are complementary: generative AI creates, agentic AI acts.

How is Levi's using agentic AI?

Levi Strauss & Co. is developing an agentic AI super-agent as part of its multi-year digital transformation journey. To automate tasks enterprise-wide, the super-agent acts as a middleman to individual specialised sub-agents, creating new efficiencies across the business and simplifying complex and repetitive work.

The agentic AI platform is built around a single super-agent to automate task-driven work throughout the entire Levi Strauss organisation. The super-agent is embedded within Microsoft Teams and powered by Azure. The super-agent was rolled out in early 2026, with expansion to global offices throughout the year.

The companies designed the platform to integrate a network of AI sub-agents covering functions across IT, human resources and operations. STITCH provides Levi's store teams with on-demand access to product information, operational procedures and training materials through a mobile app.

What is Walmart's Sparky and how does it work?

In June 2025, Walmart launched Sparky, its agentic AI shopping assistant designed to replace keyword search with a service experience. Instead of browsing product lists, customers could state goals such as hosting a cookout or planning weeknight dinners, and Sparky would plan, reason and act to deliver a complete solution.

Walmart CEO John Furner said in April that app users who interact with the Sparky AI agent spend approximately 35 per cent more than those who do not. About half of Walmart app users have interacted with Sparky. Customer engagement with the tool is up, and the average order value for Sparky users is about 35 per cent higher compared to non-Sparky customers.

Walmart's AI shopping agent, Sparky, can now use images uploaded by the user to answer questions or create shopping lists. These are two of several new capabilities Walmart has added to Sparky ahead of the holiday shopping season. Walmart is also expanding the availability of Sparky beyond customers in the United States, making it available to those in Canada and Mexico as well as Sam's Club members in the U.S.

When should fashion retailers use generative AI versus agentic AI?

Fashion businesses deploy generative AI when they need content creation with human review and agentic AI when they need autonomous process management. The choice depends on control requirements and workflow complexity.

Use agentic AI when managing multi-step operational processes. Use generative AI when human review is mandatory. Generative AI suits tasks including product descriptions, marketing copy, visual merchandising content and customer service drafts. A merchandiser prompts the system, reviews the output and approves or edits before use.

Agentic AI fits workflows where speed, scale and consistency matter more than creative judgement. Supply chain adjustments, inventory rebalancing, dynamic pricing, order routing and returns processing benefit from autonomous execution. The system monitors conditions, evaluates options and acts within predefined boundaries without human approval for each decision.

• Generative AI use cases: Product descriptions, styling recommendations, social media content, email campaigns, size guides, chatbot responses
• Agentic AI use cases: Demand forecasting, replenishment orders, price optimisation, fraud detection, workflow orchestration, cross-functional task coordination

Generative AI tends to sit alongside a person, speeding up their work. Agentic AI tends to run a process, with people overseeing it.

How big is the opportunity for agentic commerce in fashion?

Shopping-related searches on generative AI platforms grew 4,700 per cent between 2024 and 2025, according to the McKinsey and Business of Fashion State of Fashion 2026 report. Just over half, 53 per cent, of US consumers who used AI for search in Q2 2025 also used it to help them shop.

Agentic AI will shift the underlying economics of digital commerce. The growth of agentic commerce is set to accelerate in the second half of the decade as large language model-powered shopping becomes mainstream, with a potential value of 3 to 5 trillion dollars in 2030 if adoption is similar to mobile commerce in the 2010s.

Nearly a quarter of global consumers already rely on generative AI as their main starting point when shopping. Agentic AI is reshaping how people work and collaborate. AI is also transforming how people shop. Customers are turning to large language models to search for products, compare offerings and receive tailored recommendations.

According to IDC projections, agentic AI will represent 10 to 15 per cent of IT spending in 2026 and grow to 26 per cent of budgets, approximately 1.3 trillion dollars, by 2029.

What should fashion executives prioritise now?

The real shift is from generative to agentic AI: systems that execute complete workflows across platforms without human handoffs. Fashion businesses must assess which functions benefit from content creation and which require autonomous execution, then build infrastructure accordingly.

In the era of agentic commerce, optimising for engagement from shopping agents will be a high priority for nearly every brand and retailer. Set your agentic AI strategy now, ahead of mass adoption, whether by optimising for third-party agents or developing proprietary ones in cases where it is advantageous.

Companies should optimise for agent interaction to maximise discovery in 2026 by auditing content and digital infrastructure and structuring product content and data sharing feeds so AI agents can interpret and display the product catalogue. AI surfaces products it can understand, which places more weight on structured attributes and clean metadata for search. Brands must ensure their product information is consistent, complete and easily interpreted by the likes of ChatGPT, Claude, Gemini and Grok.

Fashion retailers should run pilots for both technologies in parallel. Deploy generative AI where creativity and brand voice matter. Deploy agentic AI where operational efficiency and autonomous decision-making deliver value. Most organisations will operate hybrid systems where agentic workflows call generative models for specific content tasks within larger automated processes.