Powerful retail AI agents work quietly in the background to help shoppers and retailers enjoy a smarter and more efficient buying and selling experience. They are quickly changing how companies order stock, track inventory, and sell products and services. Like the silent weekend worker who gets little fame, these AI agents automatically work independently to improve operational efficiency and increase sales. Meanwhile, AI shopping agents are helping consumers with many buying needs. Unlike retail AI agents that work in the background, AI shopping assistants show consumers new products related to their interests and purchase history. When combined, retail AI agents and AI shopping assistants match sellers to buyers in crowded eCommerce markets.
AI agents are everywhere. In the warehouse, they automatically track inventory and place orders. Their algorithms help control logistics by determining when stock should be replenished, making critical stock available in-house without spoilage or excessive reserve inventory. When shopping online, buyers use AI shopping agents like Perplexity to get detailed comparisons of products they’re interested in purchasing. While checking out online at some major retailers, shoppers get instant requests from AI agents to provide feedback about transactions. These agents collect in-the-moment responses that give customer service teams instant feedback on buyer reactions.
The use cases for AI agents continue to grow, and retailers and customers both have reasons to be excited about AI agent benefits. Here’s a deeper look at types of retail AI agents, their functions, and why they deliver real value to the industry.
What are AI agents?
Retail AI agents are intelligent systems designed to assist customers, enhance shopping experiences, increase back-office efficiency, and automate retail operations. Agents are task-specific; a customer-facing agent is not the same as an agent for replenishment. Yet, AI agents may be combined into a larger, orchestrated agentic AI workflow to expand their capabilities.
AI shopping agents
Two kinds of AI agents in retail serve different purposes: one for companies, the other for customers. The latter are AI shopping agents. Customers use these AI shopping tools on personal devices. They are designed to enhance the shopping experience, such as by making online comparisons easier and faster. Ways that customers use AI shopping assistants include:
- Product discovery: AI shopping agents can help buyers quickly find a wider selection of products, or a specific subset of products, related to their search, and similar and related products they might consider.
- Purchase decisions: Consumers can use an AI shopping agent to make detailed product comparisons to narrow options.
- Personalized recommendations: Based on previous purchases and user preferences, AI agents for eCommerce offer product and service recommendations that fit a buyer’s interests and needs.
- Alternative interactions: Through kiosks, virtual assistants, chatbots, and other devices, consumers use AI shopping assistants — many of which require a subscription — to get details about products and other information such as return policies.
Operational AI agents
The second kind of AI agents used in retail are owned and operated by retail companies. These AI agents perform different tasks depending on whether they are customer-facing or operational. A customer-facing AI agent for retail:
- Helps navigate inventory
- Personalizes the shopping experience
- Recommends similar or complementary products
- Offers incentives to discover, consider, and/or buy a retailer’s priority products, such as items offered for closeout or clearance
- Takes orders and offers tracking and return services
- Collects in-the-moment feedback on the customer’s shopping experience
- Provides customer service
Meanwhile, retailers use operational AI agents, such as eCommerce agents, for behind-the-scenes activities. These AI agents use proprietary (first-party) and third-party data to make decisions. They are often integrated into a retailer’s other systems, such as ERP, CDP, PIM, POS, OMS, WMS, RMS, and supply chain solutions. Retailers use operational AI agents for:
- Marketing segmentation
- Get product information
- Inventory management
- Demand forecasting
- Fraud detection
- Stock replenishment
- Order fulfillment, including warehouse selection and consignments
- Store operations and logistics
How has AI improved supply chain operations?
Source: State of AI in retail and CPG: 2025 trends
AI agents vs. agentic AI
As AI matures as a technology, its terminology is also developing. Yet, the difference between AI agents and agentic AI goes beyond linguistic subtlety. Agentic AI is a workflow or network of systems designed to complete many tasks with a high degree of autonomy, and an AI agent is a single system for a single task.
AI agents automate tasks with predefined parameters and goals, which means they are designed to produce a specific outcome or a certain range of outcomes. Once a data scientist trains an AI agent, it does not typically continue to adapt with new data. Instead, it uses its training data to analyze new data inputs and produce a tangible output.
On the other hand, agentic AI is a system of AI agents and non-AI agents organized in a hierarchical structure. Unlike an AI agent, agentic AI continues to evolve with new data. Rather than being programmed to accomplish specific tasks, agentic AI has the autonomy to use new data to help set its agenda and long-term goals. It can adjust its strategies over time as information changes and create new strategies, including strategies not part of the system’s original design.
An agentic AI system commonly uses three types of agents with different functional levels to complete its workflow. For example, a retailer wants to reduce delivery times without increasing costs. An agentic AI system designed to perform this task might work like this:
- High-level agents define the retailer’s goals, like optimizing last-mile delivery across different regions.
- Mid-level agents break the goals into actions, such as rebalancing stock, negotiating carrier pricing, updating delivery routes, and prioritizing specific SKUs.
- Low-level agents perform these actions by rerouting shipments, triggering store or warehouse inventory transfers, or sending notifications.
Why do retailers need AI agents?
Customer expectations are higher than ever. People want fast service, clear communication, and products made just for them. They also want an experience that is not vanilla. With stiff competition, retailers that can’t meet these expectations risk losing customers to those that can.
At the same time, retailers are under huge pressure to cut costs and become more efficient. Tariffs, tight labor markets, and inflation are squeezing millions out of budgets. Leaders are being asked to do more with less, which means finding smarter ways to run operations without sacrificing quality or customer satisfaction. This is what retail AI agents offer.
Consumers also want personalized items right now. They expect quick turnaround times and interactions that feel personal, even from a multimillion-dollar retailer serving thousands of people. Retailers must deliver customized products fast without destroying their budgets.
This is an ideal scenario for AI agents, which can automate time-consuming and manual tasks. AI agents can be used to:
- Improve the quality of product data
- Generate SEO for online product listings
- Adjust prices, including real-time price matching
- Track inventory
- Tag or categorize products
- Fulfill orders
- Prevent fraud
- Process returns
AI’s effect on revenue and operational costs
Source: State of AI in Retail and CPG: 2025 Trends
Technologies used by retail AI agents
AI agents do not bring all these benefits by themselves. They depend on connections and integrations with other systems — ERPs, CRMs, CDPs, and so forth — and first- and third-party data sources. As a result, retail AI agents use many advanced technologies to complete their tasks.
Technologies used by AI agents include:
- Machine learning: The foundation of AI agents, ML algorithms are trained with existing data to analyze inputs for changes in patterns and behaviors.
- NLP: Unlike coding, which requires specialized skills, NLP lets anyone interact with an AI agent using natural language.
- Generative AI: Through NLP, GenAI creates new content like product descriptions, marketing emails, hero banner graphics, and reports.
- Automation tools: These help retail AI agents handle repetitive tasks (such as providing order updates, scheduling shipments, and following up with customers) without human intervention.
- Computer vision: With its ability to interpret visual data, this technology is useful for recognizing low inventory levels and detecting in-store activity.
- IoT: The Internet of Things connects physical devices like smart shelves, sensors, and wearables to give AI agents — and AI shopping agents — real-world context.
- Robotics: Like automation, robots can be used for routine or dangerous tasks—such as picking, packing, restocking—and offering customer assistance.
Top 5 GenAI use cases in 2024
Source: State of AI in retail and CPG: 2025 trends
AI agents in use
Many retailers are already seeing success with AI agents, which have been integrated into popular eCommerce platforms, like Shopify and WooCommerce, and Google’s search engine. Wally, Walmart’s operational AI agent, is an AI digital assistant for the store’s associates with in-store tasks like managing inventory, locating products, and providing customer support. Wally uses real-time data from across Walmart’s network to make suggestions. These include restocking, tracking backroom inventory, or answering the associates’ operational questions. Meanwhile, Walmart has launched a GenAI-powered search assistant for customers. This AI shopping agent lets them use conversational AI to get help with grocery shopping and other purchases. It can create a list of items for an event, outfit a room, offer suggestions for clothes, or plan supplies for a vacation.
Amazon also has its own AI shopping agent. Buy For Me selects items that match a customer’s needs and preferences. And instead of searching with traditional keywords, Buy For Me users can search using full sentences or casual phrases. AWS also offers Amazon Lex for Retail, an operational AI agent designed to automate customer service tasks. Built on the same AI-powered technology used for Alexa, Lex for Retail integrates into a company’s existing systems to track orders and returns, answer FAQs, and schedule deliveries. With Lex for Retail, companies can offer customers faster support 24/7 without the need for additional personnel.
Need for custom AI agents
Off-the-shelf AI agents are readily available but often fall short of expectations. Businesses need something that will work with their existing systems to achieve their AI goals. Generic models are not designed to integrate complex internal processes, proprietary competitive differentiators, industry and regulatory requirements, unique customer demands, or other customizations. Companies need a custom AI agent built by an experienced retail software development company like Intellias to stay competitive.
Developing custom AI agents also helps companies make other changes that improve their level of digital maturity and help them grow. By democratizing data and opening access to people and processes that need it, companies become better prepared to make data-driven decisions. Custom retail AI agents can integrate with internal systems, tap into first- and third-party data for smarter decision-making, and adjust as business strategies change. These custom AI agents also create real opportunities for competitive differentiation.
Steps for implementing retail AI agents
Implementing AI agents requires the right strategy and expertise. Working with a trusted partner like Intellias helps businesses quickly deploy and scale AI systems with a focus on real results. Here’s how a retailer would typically implement an AI agent:
- Select the right partner: Choose a technology partner like Intellias with proven experience building custom AI solutions for the retail industry
- Define goals and success metrics collaboratively: Set early, clear expectations with IT and a partner like Intellias to be sure AI agents deliver measurable business value, not just technical milestones
- Collect and label data: Gather, clean, and categorize high-quality data that will be used to train the AI agent
- Build and test: Design a custom AI agent, then rigorously test it to ensure reliable performance
- Integrate with systems: Connect AI agent to internal systems with the help of a technical partner like Intellias that understands complex connections and integrations
- Refine continuously: Update and regularly train your custom AI agent models to ensure they are efficient
Challenges to using retail AI agents
Before a retailer builds custom AI agents, they must consider many risks and challenges. While AI agents promise big efficiencies, the upfront investment can be substantial. Retailers need to look at the true cost of an AI agent, including organizational change management, to understand how it might affect their business. They must also ensure responsible AI use so AI agents operate ethically, minimize harm, and create real value for retailers and society.
Regulatory compliance is becoming another area to watch as more retailers adopt AI agents. Governments are moving quickly to set new rules for how AI systems can operate, and retailers need to stay ahead of these changes or risk costly fines and reputational damage. In addition to compliance, there’s growing pressure to ensure decision-making through AI agents is transparent and traceable. Retailers must know how an AI agent made a recommendation or took an action, especially in sectors where accountability is critical.
Bias and accuracy also remain concerns, as AI agents are only as good as the data and models behind them. It’s essential to monitor for unintended bias and work to maintain high levels of prediction accuracy. AI models can also experience model drift, when their performance degrades over time because new data no longer matches the model’s training data. Without active retraining and updates, AI agents can lose their effectiveness.
Finally, many retailers hesitate to relinquish control to automated systems, and winning over staff requires change management. While management needs to build trust in the system, teams need clear communication, training, and ongoing support to use AI agents successfully.
The future for AI agents in retail
AI agents in retail have already changed how businesses operate, but more is ahead. As AI capabilities advance, retail is moving toward more powerful, connected, and autonomous systems that change how retail strategies are planned and executed.
Shifting from AI agents to agentic AI systems
The next generation of retail AI will focus on agentic AI systems and agents with greater autonomy. Instead of completing pre-determined tasks, these agents will respond to real-time shifts in market conditions and learn without human intervention. With an agentic AI system, retailers can achieve greater adaptability, real-time agent responses, and stronger workflow automation.
Embedding intelligence across systems
Rather than treating AI agents as separate tools, retailers can embed them directly into their core systems. Platforms for forecasting, inventory, marketing, commerce, order management, customer service, POS, and clienteling all can have native AI agents.
Orchestrating agentic AI workflows
When businesses shift toward agentic AI and embed AI agents into new and existing retail solutions, they create an agentic AI workflow. Soon, a coordinated network of connected agents will plan and execute retail strategies without human intervention.
Final thoughts
As retail AI agents evolve, companies that adopt them early will see the greatest improvements in efficiency and more satisfied customers. Embracing retail AI agents and AI shopping agents also prepares businesses and consumers for the future of agentic AI.
Put AI agents to work in your retail environment. Contact Intellias to learn how a custom AI solution can keep your customers loyal and your brand competitive.
