Artificial intelligence has become such an integral part of our daily routines that it’s hard to remember what life was like before tools like ChatGPT, Claude, and Midjourney existed. But while AI has proven its merit by streamlining workflows for businesses and helping consumers navigate the bottomless repositories of information online, it has limits.
Most AI tools still rely heavily on human oversight for a host of tasks, but this is changing. AI is evolving beyond large language models (LLMs) and predictive systems into “agentic AI,” a tool that can do much more than process and interpret information — it can act with agency.
Agentic AI can make self-directed decisions and execute tasks autonomously. It can, for example, come up with a grocery list for you then order those groceries on your behalf, without any input from you. This isn’t the stuff of science fiction movies. It’s a capability both Mastercard and Visa recently rolled out. It’s as remarkable as it sounds, and it’s poised to transform the way businesses operate.
What Is Agentic AI?
Agentic AI is a novel technology that actively drives outcomes. It can set goals, make decisions, and take autonomous action without input from humans. This allows agentic systems to work independently toward defined goals and outcomes.
Agents Are Not Apps — They’re Decision-Makers
Agentic tools go beyond the static scripts of AI chatbots. Where a chatbot might direct you to reset your password, an AI agent can follow through on multi-step tasks — like detecting unusual login behavior, assessing risk based on recent activity, and triggering a policy-based workflow to lock access or recommend a password reset.
In ecommerce product search, agents can go beyond recommending products based on browsing history and make the kind of judgement call typically reserved for humans. Let’s say a shopper wants some suggestions about what stocking stuffer to buy their teen.
An agentic system will analyze a retailer’s catalog then determine what products are appropriate, eliminating obvious ones (e.g., a large-screen monitor, a purse, etc.) but also less obvious ones like an iPhone or diamond bracelet which could easily fit into a stocking, but aren’t exactly small ticket items. This requires human-like reasoning about context, size, and gift-giving traditions.
Agentic AI vs. GenAI: What’s the Difference?
Generative AI excels at creating content, answering questions, and making recommendations. It can evaluate information and suggest next steps – like recommending the perfect stocking stuffer for your teen or suggesting ways to optimize your website’s SEO.
Agentic AI goes beyond suggestions to execute a task or process. Rather than just recommending the stocking stuffer, an agentic system could analyze data like your child’s age, interests, and hot trends, then purchase an item on your behalf.
Relevant reading:Agentic AI vs Generative AI: What Is the Difference?
How Agentic AI Introduces Human-Like Judgement into Systems
Agentic AI follows a sophisticated process that mirrors human decision-making. It gathers real-time data from multiple sources, including enterprise systems and customer interactions Then it analyzes this information using large language models (LLMs) and retrieval-augmented generation (RAG) to ensure decisions are based on accurate, up-to-date information
Then, it takes action, executing tasks and optimizing operations while adhering to business policies and compliance controls. This is how agentic AI can handle complex scenarios that traditionally require human judgment.
Ecommerce Use Cases that Demand Agentic Thinking
Ecommerce teams make a litany of daily decisions that require human judgment — from determining which products to promote to understanding if a winter coat is relevant for a “lightweight autumn coat” search. Traditional AI can help with these tasks — up to a point, but agentic AI can transform how these decisions are made and executed at scale. Here are some key examples:
SEO at Scale: Teaching Agents to Choose the Right Keywords and Content
SEO is a perfect use case to illustrate the usefulness of agentic AI. Let’s say you need to assess opportunities to optimize your website for search engines. An AI agent can review thousands of keywords, assess their relevance to your business, and determine which content gaps are worth addressing (and which keywords are worth focusing on). The agent can perform the actual optimization too, by generating text or entire pages. These are tasks that would take human SEO specialists weeks to complete.
Merchandising Intelligence: What Should Rank and Why?
Traditional AI can analyze trends and adjust product rankings automatically, but this is just advanced automation. For truly agentic merchandising, the system needs to make complex judgment calls about relevance and context. For example, when deciding how to rank and display products, an agentic system considers data points like sales trends and inventory levels, then makes human-like decisions about whether products truly match the searcher’s intent and context.
Precision Over Recall: Solving the “Too Much, Not Relevant” Problem.
The difference between traditional AI and agentic AI becomes clear when you look at how each handles search relevance. Traditional search might show you egg cookers, rice cookers, and waffle makers when you search for an air fryer. It casts a wide net that prioritizes showing more results over showing the right results.
Agentic AI, by contrast, makes judgment calls about true relevance. A search for an air fryer on a site powered by agentic AI, shows only air fryers regardless of how you slice it up (price, top-rated, most reviewed, etc). The system understands and maintains the core intent of your search, ensuring precision isn’t sacrificed for the sake of showing more products.
The Stocking Stuffer and Baby Shower Test: Context-Aware Relevance
Context-aware relevance is where agentic AI’s decision-making capabilities really shine. The above “stocking stuffer” example demonstrates this. It requires the agent to understand more than just product categories or prices. Similarly, when someone searches for “baby shower gifts” on a sporting goods website, an agentic system needs to understand social context, not just keywords.
Traditional AI might see the word “shower” and return camping showers, a technically accurate but contextually wrong result. Agentic AI can make the human-like judgment calls needed to understand the social context of a baby shower and return truly relevant gift options like portable highchairs or infant sleeping bags.
Relevant reading: The Rise of Agentic Commerce
From Back-End Power to Front-End Impact
Branded AI assistants like Salesforce’s Einstein and Microsoft’s Copilot are front-end agents that directly interact with users. At Coveo, we’re taking a different approach. Coveo serves as the intelligence layer for agentic AI, the brain that powers the complex decision-making happening behind the scenes.
Consider the following customer service query on a manufacturer’s website: returning a hydraulic pump from order #12345 Multiple systems need to work together to respond appropriately to this query. That involves:
- Pulling the order details
- Identifying the specific item
- Determining if the return is warranted based on common issues with hydraulic pumps
Coveo’s role is to provide the relevance and intelligence that helps these systems make better decisions, like understanding which product information is most relevant or what solutions might prevent an unnecessary return. This approach allows retailers to maintain their preferred front-end experiences while upgrading the intelligence that powers them.
Agents as the Intelligence Layer, Not the Experience Layer
Unlike branded assistants, Coveo’s value lies in enabling other systems to be smarter — not in owning the user interface. That flexibility allows ecommerce teams to keep their preferred front ends while upgrading what’s under the hood.
Why Ecommerce Leaders Should Care
Ecommerce success depends on delivering relevant experiences to every customer, a challenge that typically requires countless hours of manual work. Agentic AI addresses this by making judgement calls automatically, using the same reasoning a human would.
Instead of merchandising teams spending hours adjusting product rankings or SEO specialists reviewing thousands of keywords, agentic systems take on the heavy lifting without manual effort.
This shift from reactive to proactive optimization means sites can continuously improve without constant human intervention. Rather than waiting for analytics to show that customers are getting irrelevant search results for “stocking stuffers” or “baby shower gifts,” an agentic system can preemptively ensure contextual relevance.
Most importantly, this enables true personalization that goes beyond data points. The system can understand and act on the context of each customer interaction. It allows retailers to scale personalization with judgement, not just data.
What’s Next: The Road Ahead
The landscape of agentic AI is still in flux, with major tech players investing billions to shape the future of this promising new technology. For digital commerce teams, this creates a unique opportunity to rethink how they approach everything from SEO to merchandising to customer service.
You don’t need to overhaul everything at once. But the time to start thinking about, and preparing for, what adopting agentic AI might look like for your business. Start by identifying areas where agentic offers significant benefits over manual processes that may create bottlenecks in your operations. In the realm of ecommerce search that includes:
- More proactive and relevant tech support agents: Systems that can identify and resolve potential issues before they escalate, reducing the need for human intervention
- Agentic shopping assistance: AI that goes beyond recommending products to guide the entire shopping journey with human-like understanding of context and intent
- Case classification, guidance, and triage: Intelligent systems that package all pertinent details and route service requests to the best-qualified agent while surfacing relevant historical cases
- Intelligent, goal-driven search and discovery: Search experiences that understand true intent, ensuring results maintain precision and relevance across all sorting and filtering options
- Automated merchandising and inventory management: Systems that make human-like judgment calls about which products to promote and recommend based on factors like inventory availability, customer preferences, and trends.
- Autonomous customer retention: Predictive systems that can identify at-risk customers and take proactive steps to maintain engagement before problems arise
The winners in this next phase of AI won’t be those who build the flashiest front-end experiences, but those who best harness AI’s ability to make human-like decisions behind the scenes. Start exploring how agentic AI can transform your digital commerce operations today by speaking with a Coveo AI expert.