Agentic AI is definitely having a moment. It’s the latest AI buzzword to sweep through commerce and keynote stages, promising systems that can think, reason, and act autonomously. In commerce, this has led to a growing list of claims—and just as many misconceptions. In this article, we’ll break down the most common agentic commerce myths and debunk what’s real versus what’s still hype.
Agentic AI, the story goes, will soon transform how we shop, work, and make decisions; a kind of self-driving engine for digital life. It’s the next frontier beyond chatbots and conversational interfaces: AI that doesn’t just answer questions, but acts. And it seems that everyone from startups to major vendors is claiming to build “agentic” solutions.
In commerce especially, the market is full of agentic AI washing: bold claims about autonomous intelligence, yet the reality is often more modest. Most of what’s marketed today as “agentic AI” is neither agentic nor autonomous but conversational. Useful, yes. Transformative? Not yet.
So what does agentic AI really mean, what are some of the myths surrounding it, and how far are we from seeing it transform the buying experience in meaningful ways?
What Agentic Actually Is
To understand where the hype ends and the reality and potential begin, it helps to define agentic properly. Agentic systems are designed to plan, reason, and act toward a goal. Beyond responding to prompts; they plan, execute, and adapt along the way. Think of it as the difference between giving someone directions (a simple Q&A exchange) versus them navigating to a destination, adjusting for traffic, and rerouting on their own if they hit a dead end.
In short, an agentic system can:
- Break down a task into multiple steps
- Use different tools or data sources along the way
- Self-correct when it hits an error
- Remember context across interactions
- Refine its plan as conditions change
For example, when ChatGPT or Gemini appears to “think” — searching the web, refining its query, and re-evaluating its answer before responding — what you’re seeing is agentic behavior. It’s reasoning and adapting in real time. That pattern: plan → act → evaluate → adapt — is what separates reasoning from retrieval. A search-only system returns data that looks relevant; an agentic system judges that data, cross-checks it, and uses it as part of a multi-step solution.
What Agentic AI Is Not
That distinction matters because most of what’s called “agentic AI” in commerce today isn’t agentic at all. Retailers and vendors are rebranding conversational tools — AI-powered search, virtual assistants, chat interfaces — as “agentic experiences.” But these tools still follow linear, single-turn paths: user asks, system answers.
Examples include:
- Natural language search (“Show me blue running shoes under $150”)
- Buying advice (“What’s the difference between these two models?”)
- Order support (“Where’s my shipment?”)
All of these are valuable — and all are powered by large language models (LLMs) acting as an intelligent interface over structured data. But they are not autonomous. They don’t reason independently or take action across systems. A true agentic system makes decisions on its own. It adapts and reasons. What most companies are selling today is just smarter conversation.
In other words, a chatbot that helps you find a product or a digital assistant that answers “where’s my order?”. These are not autonomous capabilities and are guided rather than generative.
True agentic behavior, where a system can act independently across multiple steps, simply isn’t available yet for most shopping scenarios.
Even the biggest players illustrate the gap between hype and reality:
- Walmart Sparky, a GenAI shopping assistant, helps customers compare items, summarize reviews, and plan for occasions — but despite Walmart calling it “agentic,” its behavior today is still guided and scripted rather than truly autonomous. It doesn’t yet plan multi-step tasks or take action across systems.
- Amazon Rufus helps shoppers compare products, interpret specs, and understand differences between models — but it doesn’t autonomously reroute a purchase, source substitutes, or make cross-system decisions. It’s a conversational layer over Amazon’s catalog, not an autonomous agent.
- Shopify + LLM Integrationsallow shoppers to query store catalogs conversationally (“gift ideas under $50”) and get natural language product guidance. But these assistants don’t autonomously optimize carts, compare fulfillment routes, or negotiate purchases. They surface information; they don’t act on it.
These are powerful conversational tools, but not multi-step, autonomous decision-makers.
Where We Actually are in Commerce
Part of the reason agentic AI hasn’t yet broken through in commerce is that the underlying systems aren’t ready for it. Online stores and marketplaces are built around structured catalogs, controlled workflows, and tight compliance rules, a long way from the open-ended autonomy that agentic AI requires.
That’s even truer in B2B, where procurement runs on rigidity, not spontaneity. Much of it still depends on decades-old frameworks like EDI, where precision matters more than conversational reasoning.
Analyst data reinforces this. Fewer than 5% of enterprise applications today contain true AI agents, according to Gartner. They predict that number will rise to 40% by 2026, but with a significant caveat: more than 40% of agentic AI projects are expected to be canceled by 2027 due to cost overruns, unclear ROI, and lack of governance.
We’re in the early innings.
Still, the long-term potential is real. Imagine a buyer’s assistant that can automatically source substitutes when inventory runs out, compare suppliers across systems, or flag compliance risks before a purchase order is submitted. These are scenarios where agency, not conversation, could genuinely save time and money.
But to get there, organizations need to focus less on the “AI” and more on the infrastructure beneath it.
The Foundations of Agentic Readiness
For all the talk of autonomy and machine-driven decision-making, the reality of agentic AI is that its success depends less on model sophistication and more on old-fashioned data hygiene. A system can only act intelligently if it knows what it’s acting upon.
That starts with rich, structured product content. This could be technical specs, compatibility notes, warranty details, images, and buying guides for example. Most retailers and distributors still struggle here. If an agent can’t reliably tell the difference between two SKUs or identify substitutes, no amount of “intelligence” will save it.
Commerce also requires the ability to map natural language queries to catalog attributes. When a user searches for “waterproof hiking boots for wide feet,” the system needs to understand that “waterproof” maps to a material property, “hiking” to a category, and “wide feet” to a size attribute. This goes beyond keyword matching or vector similarity; it requires understanding intent, synonyms, and the relationship between language and product taxonomy. Without this foundation, even the most sophisticated agent will struggle to surface the right products or make intelligent substitutions.
Next is connected knowledge: FAQs, troubleshooting steps, documentation, manuals, videos, community insights. This is the grounding material that lets a model make sense of a buying journey, not just a catalog.
Finally, organizations need measurement frameworks to evaluate what’s working. Rather than chasing model novelty, the real questions are: Did this reduce cost-to-serve? Did it improve conversion? Did it prevent a support ticket?
Ultimately agentic systems don’t emerge from hype cycles; they grow out of infrastructure. Without that foundation, autonomy becomes a liability.
Further reading: The AI Agent Readiness Checklist for Ecommerce
Three Agentic Myths Debunked
Below are three of the most persistent agentic commerce myths we see in the market today, and why they don’t hold up in real-world commerce systems.
Myth 1: Customers are asking for agentic experiences.
Not explicitly. A Forrester 2025 Consumer Pulse Survey found that only 24% of U.S. online adults trust AI agents to make routine purchases. What they are asking for is faster, clearer, more trustworthy digital experiences. When conversational shopping tools first launched, usage surged on novelty. But industry data keeps showing that most customers still prefer structured search results and visual filtering over simulated “chat.” Convenience wins; personality doesn’t.
Myth 2: Today’s assistants are truly agentic.
Most aren’t. They simulate intelligence through pattern matching, predefined flows, or well-designed retrieval. Calling these systems “agents” is like calling a GPS a self-driving car. True agentic behavior requires multi-step planning, tool use, self-correction, and adaptive reasoning. These are capabilities that are only just emerging.
Myth 3: The technology is mature.
In reality, commerce teams are testing prototypes, not deploying autonomous systems at scale. Many vendors “demo” functions that can’t yet withstand real-world constraints: messy data, conflicting inventory signals, imperfect supplier information, compliance rules. The gap between what’s promised and what’s delivered remains wide.
The Risks and Reality Check
With each advance in tool use and autonomy, the surface area for risk expands. The first and most obvious is security. When an LLM can call external tools such as catalog APIs, order systems, and payment gateways, the attack vectors multiply. Every integration becomes a potential point of exposure. Recent incidents involving AI systems unintentionally exposing sensitive data (e.g., credit card details through poorly designed agent workflows) illustrate how quickly things can go wrong when guardrails fail.
Then there’s accuracy. A search mistake is reversible. An autonomous action is not. Recommending the wrong part number, issuing a return incorrectly, triggering a procurement action are errors that carry real operational costs.
There’s also a subtler risk: over-trust. As agents become more fluent, people assume they are more capable. They’re not. A polished answer can mask a fragile reasoning chain. In commerce, where margins can be thin and supply chains complex, overconfidence is expensive.
And finally, there’s the cultural risk: the belief that autonomy is the goal. In reality, the best AI systems today are assistive, not independent. They help humans move faster and think more clearly — they don’t replace human judgment.
The Road Ahead
Despite the hype, the trajectory for agentic AI is both steady and sensible. In the near term, expect smarter conversational interfaces that understand intent better, handle follow-ups naturally, and personalize answers using context and behavior. They’ll feel smoother, but still fundamentally assistive.
In the medium term, we’ll see semi-agentic systems capable of limited multi-step reasoning: comparing products across constraints, identifying substitutes automatically, or mapping troubleshooting flows across content sources.
And in the long term, with rigorous data foundations, governance, and API ecosystems, the promise of true agentic commerce begins to take shape. Systems will coordinate information across suppliers, logistics networks, and content repositories to offer not just answers, but solutions.
The timeline is less Hollywood and more infrastructure: slow, systemic, and grounded in high-quality data.
Building Toward Agentic: The Coveo Approach
At Coveo, we’re not chasing autonomy for autonomy’s sake but focused on solving the foundational problems that will make agentic experiences genuinely useful, accelerating purchase decisions without disrupting high-performing discovery paths.
Our “Intent Box” approach exemplifies this philosophy. Instead of forcing every interaction through a chatbot, it interprets what a buyer or customer is actually trying to do and adjusts automatically. Whether that means returning a filtered product list, a product comparison grid, or providing product guidance through a multi-turn conversation. It’s intelligent orchestration based on intent, not a one-size-fits-all conversation.
We’re also bringing true agentic capabilities to merchandising teams enabling them to work more efficiently, through natural language inputs, and also take action through intelligent agents that execute tasks on their behalf.
The Bottom Line
Agentic AI will likely reshape digital commerce, but not in the way the hype suggests. Its greatest value won’t come from machines acting independently, but from machines that think with us, helping people make better decisions with more context and less friction.
Debunking these agentic commerce myths is essential for building systems that are realistic, reliable, and ready for production. For now, the smartest strategy isn’t to chase autonomy. It’s to build the systems, data, and governance that will eventually make autonomy safe, useful, and real.
In other words: before AI can act for us, it has to truly understand us and the world it’s acting within.