Agentic AI in Ecommerce: Driving Smarter Decisions and Seamless Experiences

Picture this: A shopper lands on your ecommerce site and is instantly guided by an AI-powered agent that not only understands their intent but takes action, comparing prices, personalising product recommendations in real time, streamlining the checkout process, and even initiating post-purchase support. That’s the power of agentic AI.

Unlike traditional artificial intelligence systems that rely heavily on human input to function, agentic AI leverages autonomous agents designed to operate independently across complex workflows. These AI agents interpret goals, make autonomous decisions, and execute actions without constant human intervention. For ecommerce professionals, agentic AI represents a leap forward in automation, customer engagement, and decision-making. As businesses face rising customer expectations and tighter competition, agentic AI offers the opportunity to optimise operations, deliver personalised experiences, and build a scalable ecosystem of intelligent tools that drive growth. Thought leaders across platforms like LinkedIn are already discussing how this shift is redefining ecommerce strategy.

What is agentic AI?

Agentic AI refers to a new class of AI systems designed to act with autonomy, capable of setting goals, making decisions, and taking action without direct human oversight. Rooted in autonomous agents and advances in machine learning, this form of AI goes beyond simple automation to deliver intelligent, proactive behavior. Whereas traditional AI is typically rule-based and reactive, such as automated chatbots with pre-defined responses that don’t evolve over time, agentic AI uses algorithms, real-time data, and orchestration capabilities to dynamically adapt and respond. It’s not just following instructions, it’s identifying the best course of action based on context.

Similarly, while genAI focuses on creating content using large language models (LLMs) like ChatGPT or tools from OpenAI, agentic AI layers on goal orientation and autonomy. It doesn’t just generate responses, it executes multi-step tasks to support outcomes across business operations.

For example, genAI could be used to give you a list of all the top-rated Italian restaurants open late on Fridays, but in the future, agentic AI could look at your schedule and make a reservation every Friday night for whichever restaurant has availability.

This shift has powerful implications for decision-making and task execution. Agentic AI enables commerce platforms to anticipate needs, automate repetitive tasks, and proactively engage shoppers, optimising both the customer journey and backend workflows.

The rise of agentic AI in ecommerce

The adoption of agentic AI is accelerating across retail and ecommerce, driven by growing consumer expectations for personalised, real-time shopping experiences and operational agility. Where early uses of AI focused on rule-based chatbots and basic automation, today’s AI agents can independently optimise merchandising, manage customer inquiries, and execute complex workflows with minimal human intervention.

Connecting the dots with agentic AI.

This shift is fueled by several key drivers. First, shoppers now demand hyper-personalised interactions, from product recommendations to dynamic price adjustments, delivered seamlessly across digital touchpoints. Agentic AI enables ecommerce platforms to tailor offers, guide product discovery, and optimise the customer journey in real time. 

Google’s AI Mode is an example of agentic AI already being incorporated into the ecommerce shopping journey. It introduces powerful tools like price tracking and automated checkout that let shoppers set specific product preferences, such as desired sise, color, and budget. Once these criteria are saved, the AI keeps an eye on listings across the web. When a match is found and the price drops, AI Mode can:

• Send a price drop notification

• Automatically add the item to the retailer’s cart

• Pre-fill checkout information

• Finalise the purchase securely through Google Pay

Importantly, users retain full control. AI handles the heavy lifting, but shoppers still review and approve all purchase details before anything is completed.

Appearing in AI search results.

Second, the rise of omnichannel commerce has added layers of complexity to backend operations. Retailers need scalable automation that integrates smoothly with APIs across multiple channels and platforms, like Amazon and Salesforce, plus a real-time ecosystem of tools to support a unified brand experience.

Retailers need to account for AI search platforms in their channel mix as well. Shoppers are searching on AI platforms like ChatGPT and Perplexity to find product recommendations, so how can brands make sure to appear in those search results? 

AI platforms primarily find products in three ways:

1. AI platforms scrape unstructured product data from websites.

AI crawls sites across the web to find products that match a user’s query. This method gives retailers the least control over how their product listings show up in AI search results. Retailers need to make sure their data is structured and optimised consistently so that AI can extract accurate and relevant data from sites, including third-party marketplaces like Amazon or ad channels like Google Shopping.

2. Model Context Protocols (MCPs) provide AI platforms a structured blueprint for finding product data.

Ecommerce platforms are developing API-based frameworks to give AI platforms more direction for crawling and retrieving site data. Retailers are still dependent on AI platforms to find their site, but they have more control over the data that AI picks up for recommending products. 

3. Syndication platforms like Feedonomics provide data directly to AI platforms. 

For maximum control over the product data that is used by AI in product recommendations, retailers can use a syndication platform to create highly structured and complete product feeds, then deliver them directly to platforms like Perplexity and OpenAI. Feedonomics and BigCommerce recently announced a partnership with Perplexity to enable retailers to provide product data directly to the AI platform, enabling better shopping experiences for users and better visibility for retailers.

“Some aspects of the AI future are already clear. Consumers want agentic experiences throughout their shopping journey, and they turn to Perplexity for accurate answers they can trust,” said Taz Patel, Head of Advertising and Shopping at Perplexity. “When our systems can ingest clean, well-organised product information with rich attributes, consistent taxonomy, and up-to-date availability, the results speak for themselves: more relevant search experiences, higher conversion rates and better alignment with shopper intent. With Feedonomics delivering AI-ready data to Perplexity’s powerful and highly-trusted answer engine, we are setting a new standard for ecommerce search."

Streamlining and speeding up operations.

Finally, businesses are under pressure to reduce operational costs while increasing efficiency. According to Gartner, by 2029, agentic AI will autonomously resolve 80% of common customer service issues, offering up to a 30% reduction in costs and significantly improving response speed. These capabilities also extend into fintech, powering fraud detection, payment optimisation, and intelligent credit risk modeling.

Payment providers such as Mastercard are also exploring agentic AI to enhance fraud prevention and streamline digital payments through autonomous risk assessments.

Unlike traditional AI, which is often limited to task-specific responses or content generation through generative AI, agentic AI takes proactive control. These systems don’t just react, they plan, execute, and adapt, providing a powerful foundation for AI-driven digital commerce transformation.

Top use cases for agentic AI in ecommerce

AI-powered shopping assistants.

Agentic AI enables a new generation of AI-powered assistants that stay active throughout the customer journey, providing continuous, context-aware support. These are not basic AI chatbots, they’re adaptive, autonomous agents that guide users in real time, tailoring experiences with minimal human input.

By integrating with customer data and understanding real-time context, these assistants personalise product discovery, offer in-cart recommendations, and drive upsell opportunities. Whether helping users compare prices, suggest complementary products, or navigate complex catalogues, these AI agents act as proactive guides, powering the next wave of conversational commerce.

Autonomous customer support agents.

Agentic AI is transforming customer support by enabling AI agents that can resolve issues, answer questions, and manage escalations, all with minimal to no human intervention. These autonomous systems can instantly handle FAQs, initiate live chat, or even deploy voice-based bots that guide customers through troubleshooting or post-purchase steps.

More advanced use cases include proactive outreach, where AI-driven systems monitor customer behavior and step in when users show signs of friction, such as abandoning a cart or encountering a payment error. These agents don’t just react to support tickets; they anticipate needs and act in real time, improving the overall customer experience and the quality of customer interactions.

This level of automation not only boosts response speed and consistency, but also delivers significant operational cost savings. By handling repetitive inquiries and intelligently routing complex cases, businesses free up their human agents to focus on higher-value interactions.

BigCommerce’s API-first architecture and robust partner ecosystem make it easy to integrate these AI-powered support solutions. Whether through tools like OpenAI-based chat interfaces or enterprise platforms like Salesforce, brands can deploy scalable, agentic support systems that grow with their business, whether you're a global retailer or an agile startup.

Intelligent merchandising.

With agentic AI, ecommerce merchandising becomes a dynamic, data-driven process. These AI agents can autonomously adjust product placement, price, and promotions based on real-time shopper behavior, trends, and inventory levels, with no manual intervention required.

Use cases include demand forecasting that anticipates spikes or slowdowns and automatically optimises stock levels; A/B testing automation that quickly identifies the highest-performing product layouts or promotional banners; and contextual search that delivers more relevant results based on user behavior, location, and preferences. These capabilities help retailers deliver frictionless, personalised experiences that respond instantly to market conditions.

For ecommerce brands using BigCommerce, this type of automation is already within reach. Our flexible SaaS platform and headless architecture support integration with AI-driven merchandising solutions, giving brands the flexibility to deploy the tools they need without being locked into rigid systems. The result: higher conversion rates, leaner inventory management, and a merchandising strategy that evolves as quickly as your customers do.

Predictive personalisation.

Agentic AI enables next-level predictive personalisation by analysing real-time and historical data to anticipate what individual shoppers want, often before they know it themselves. By tapping into behavior patterns, purchase history, and even external signals like seasonality or geographic trends, AI agents can deliver hyper-relevant content and offers across every channel.

Use cases include personalised homepage layouts that adapt based on browsing behavior, automated email sequences tailored to individual engagement history, and dynamic retargeting ads that reflect the user’s current interest or lifecycle stage. These experiences aren’t just reactive, they’re proactively designed to keep customers engaged, improve SEO visibility, and move them toward conversion.

BigCommerce supports these personalisation strategies, allowing brands to connect seamlessly with third-party personalisation engines. With our platform’s API flexibility and headless capabilities, ecommerce businesses can orchestrate highly-customised customer journeys that drive deeper engagement and long-term loyalty.

Benefits of using agentic AI for ecommerce brands

Agentic AI marks a shift from reactive automation to proactive, goal-driven intelligence, giving ecommerce brands a powerful new tool for growth. Unlike traditional systems that rely on manual rules or one-off triggers, agentic AI empowers autonomous agents to make decisions, adapt in real time, and take meaningful actions across the customer journey.

For ecommerce businesses, the value is both strategic and operational. On the backend, agentic AI helps streamline complex workflows, reduce repetitive tasks, and enhance agility across inventory management, pricing, and support operations. On the frontend, it delivers hyper-personalised, AI-powered experiences that increase engagement, lift conversion rates, and support high-impact initiatives like dynamic pricing, intelligent routing, and personalised merchandising.

By embracing agentic AI, brands position themselves to move faster, scale smarter, and compete in a rapidly evolving digital landscape. BigCommerce makes this transformation accessible through our open architecture, robust APIs, and a flexible ecosystem of AI-driven partners, ensuring ecommerce brands have the tools they need to stay ahead.

Streamlined operations and reduced manual effort.

One of the most immediate benefits of agentic AI is its ability to handle the time-consuming, repetitive tasks that often slow down ecommerce teams. These autonomous agents work continuously in the background, freeing up staff to focus on strategy and innovation rather than manual upkeep.

Tasks like catalog management, updating product listings, tagging new inventory, or syncing data across channels, can be fully automated based on rules and real-time inputs. Similarly, customer segmentation becomes more precise and dynamic, with AI analysing behavior and demographics to group customers for campaigns or promotions without human sorting.

Fraud detection is another area where agentic AI excels. By constantly monitoring transactions, identifying anomalies, and supporting tokenisation to protect sensitive customer data, these systems reduce risk without the need for round-the-clock oversight.

With these capabilities, ecommerce brands can dramatically increase operational efficiency, reduce errors, and scale faster, all while maintaining tighter control and visibility.

Elevated customer experience through personalisation.

In today’s ecommerce landscape, personalisation isn’t a bonus, it’s a baseline expectation. Agentic AI raises the bar by tailoring every touchpoint in real time, creating seamless, intuitive experiences that keep shoppers engaged and satisfied.

From personalised landing pages to dynamic product recommendations and smart content delivery, AI agents adapt to each customer’s behavior, preferences, and stage in the customer journey. This means a first-time visitor might see a curated homepage focused on bestsellers, while a returning customer is shown in-cart reminders or loyalty-driven upsells.

Because agentic AI operates continuously, it refines its understanding with every interaction, pulling from customer data, purchase history, and even contextual signals like location or time of day. The result: more relevant interactions, faster paths to purchase, and a customer experience that feels genuinely intuitive.

Cost savings and improved scalability.

As ecommerce brands grow, so do operational demands, often requiring larger teams to manage support, merchandising, and logistics. Agentic AI flips this equation by allowing businesses to scale without growing overhead at the same rate. By automating critical functions, brands can reduce reliance on manual labor and reinvest in strategic growth.

One of the clearest examples is in customer service. With autonomous agents resolving FAQs, routing support tickets, and proactively assisting shoppers, brands can significantly reduce labor costs while delivering faster, more consistent service.

Beyond support, agentic AI also reduces costs in areas like catalog updates, fraud detection, and campaign management, enabling brands to run leaner while expanding their reach. 

Smarter decision-making with real-time data.

Agentic AI empowers ecommerce brands with a continuous flow of actionable insights, enabling faster, more accurate decision-making across the business. By processing real-time data from shopper behavior, inventory levels, sales trends, and external signals, these systems surface opportunities and flag issues before they impact performance.

Whether it's adjusting pricing in response to demand, optimising campaigns mid-flight, or fine-tuning product recommendations, AI-driven decisions happen instantly, without waiting for manual analysis or delayed reports. This agility allows brands to stay ahead of shifting consumer expectations and market dynamics.

The final word

Agentic AI is more than just the next phase of automation, it’s a transformative force reshaping how ecommerce brands operate, engage customers, and scale. As the future of commerce becomes increasingly autonomous and AI-driven, agentic systems will define how brands compete and grow.

As retailers face increasing complexity and competition, the brands that embrace AI agents, integrate intelligent systems, and leverage platforms like BigCommerce will be positioned to lead. With the right ecosystem of tools and the flexibility to innovate, plus strategic partnerships with AI solution providers, ecommerce businesses can unlock greater efficiency, improved experiences, and lasting competitive advantage.

FAQs about agentic AI ecommerce