How Retail Leaders Are Mistaking Interfaces for Autonomy

by Rafael Esberard

Lately I have been vetting and analysing a wave of promised “agentic” solutions in retail. At NRF here in New York City, the pattern was impossible to ignore. Almost every booth carried the word AI. A large majority proudly displayed agent or agentic. The signal was clear. The market has decided that “agent” is the next badge of innovation.

I did not walk the floor as a spectator. I was there with a responsibility. My job is to evaluate these solutions rigorously before recommending anything to my clients. I sat through demos. I asked uncomfortable questions. I pushed past polished scripts. When you represent companies that will invest serious capital, you learn to separate theater from capability.

Here is the uncomfortable truth… Most of what is being presented as an AI agent today is not an agent. It is a chatbot, enhanced with an LLM, sometimes connected to a tool, but still fundamentally reactive. The word agent is being stretched beyond its meaning because it sells. And when a word sells, it spreads quickly.

If we do not define this properly now, executives will make expensive decisions based on a label rather than a capability. So let’s step back and review some definitons…

The Core Distinction (chatbot vs agent)

The Chatbot is a reactive system. It waits for you to ask. You type a request, it responds. You give another instruction, it executes a bounded action. The user drives the sequence. Even when powered by a large language model, it remains fundamentally conversational. It answers, suggests, and occasionally triggers a predefined action. It does not own the outcome.

The Agent is different in principle. An agent is a system that owns an end to end outcome, not a single command. It can plan multi step work, execute across systems with proper permissions, run asynchronously, and handle exceptions without requiring the user to guide every move. The user defines the objective. The agent advances the task.

Here are my initial line in the sand tests:

• If the user must drive every step, it is a chatbot.
• If the system cannot run without the chat window open, it is not an agent.
• If it cannot handle exceptions and recover intelligently, it is not an agent.

This distinction matters because language can create the illusion of capability. A fluent interface feels intelligent. But fluency is not autonomy. A conversational wrapper does not transform a reactive tool into an outcome driven system. Executives must discipline themselves to ask one simple question: who is really doing the work, the user or the system?

The Hype Myths (dismantling)

Let us dismantle the most common myths, because these are the exact claims being used to sell “agentic” solutions right now.

Myth 1: “If it uses an LLM, it is an agent.” An LLM is not an agent. An LLM is a language and reasoning engine.

• It can write, summarize, explain, and recommend
• It can sound confident
• It can even propose a plan

But if it cannot execute that plan end to end, it is still a chatbot. A smarter chatbot, but a chatbot.

Myth 2: “If it calls an API once, it is an agent.” Calling an API is not agency.

• A single API call is an action
• Agents are systems of actions
• Agency is not “can it do something,” it is “can it complete the outcome”

Tool calling is a feature. Agents require orchestration.

Myth 3: “If it can add to cart, it is an agent.” This one is the easiest to expose.

Retail has had:

• intent recognition
• conditional bots
• scripted automation
• add to cart triggers

for well over 15 years.

So when someone shows “add to cart” as agentic, you are not seeing a breakthrough. You are seeing a familiar capability with a new label.

Myth 4: “Chat interface equals agentic workflow.” A chat window is not a workflow engine.

• Chat is an interface
• Workflows require state, permissions, monitoring, exception handling, and recovery
• Chat makes weak systems look powerful, because language is persuasive

And that is where executives get trapped.

A real example I just saw this week I watched a demo from a well known retail search vendor now branding an “agentic experience.” The demo was a chat window. The user typed: “Please add this product to the cart for me.” AGENTIC!!?? Ps: And the add to cart button was literally one inch away. It was a high-level session, with extreme hi-level retail executives and consultants present. That is not an agent. That is theater. And theater is expensive when you mistake it for capability.

The Maturity Ladder

To bring discipline to this conversation, I use a simple maturity ladder. Not to criticize vendors, but to clarify where a solution truly sits.

1. Rules based bot: Predefined flows, scripted responses, conditional logic. Intent recognition, basic understanding of user intent, mapped to predefined actions.
2. LLM Chatbot: Natural language reasoning, dynamic responses, better context handling, still reactive. Can do tool calling assistant, can trigger APIs or systems when prompted, executes single bounded actions. (90% the “agentic” promisses I have seem solutions in the market today have not crossed it further)
3. Supervised Agent: Can plan multi step workflows, operate across systems with permissions, handle exceptions, and run asynchronously, with oversight.
4. Autonomous Agent: Owns the outcome end to end, manages execution, monitors performance, and escalates only when necessary.

The critical shift happens between tool calling assistant and supervised agent. At level 2, the user still drives the process. The system reacts and executes isolated commands. At level 3, the system begins to plan. It sequences actions. It checks results. It recovers from errors. It runs without constant prompting. It operates within defined permissions and governance structures.

Conclusion – Let’s Bring Home

This is not a semantic debate. It is a capital allocation issue. When executives confuse chatbots with agents, two predictable things happen:

• First, companies overpay for rebranded interfaces. The price reflects the promise of autonomy, but the capability remains reactive. You end up funding a better conversation layer, not a system that reduces labor or owns outcomes.
• Second, strategy gets distorted. Teams are told that “agents are coming,” expectations rise, roadmaps shift, and real infrastructure work, integration, permissions, monitoring, orchestration, gets postponed. Capital is deployed toward visible demos instead of durable capability.

Language is persuasive. A fluent interface creates the perception of intelligence. But perception does not execute workflows. And perception does not generate ROI. So here is the discipline I recommend to my clients before approving any “agentic” investment.

Ask for evidence of these five capabilities:

1. End to end outcome ownership, not isolated task execution
2. Asynchronous execution without constant user prompting
3. Exception handling and recovery logic
4. Persistent memory and personalization across time
5. Evaluation and monitoring with measurable reliability

If a vendor cannot clearly demonstrate these in production, not in theory, you are not buying an agent. You are buying a chatbot.

The market will continue to use the word agent because it signals progress. But as leaders, we are responsible for precision. Most of what is called agent today is not. If you must type every step, it is not an agent. If it cannot run without the chat open, it is not an agent.

Stop buying interfaces. Start buying outcomes. And internally, stop using the word agent until the capability earns it.

Thank you!

—

Rafael Esberard is a Digital Innovation Architect and Strategic Consultant with over 20 years of experience in the eCommerce and Software Development industry. As the founder of KORE Business, he helps companies design, govern, and evolve their digital ecosystems through a pragmatic, business-driven approach to composable, MACH architecture, Agile and AI integration. Rafael is a MACH Ambassador and works alongside retailers and industry leaders to guide the selection, validation, and orchestration of best-fit solutions across complex multi-vendor landscapes, ensuring scalability, agility, and long-term ecosystem health. His expertise spans omnichannel strategies, AI-driven ecosystem optimization, and accelerating time-to-value and time-to-market across digital transformation projects. By bridging technology evolution with real-world business needs, Rafael enables clients to transform ambition into sustainable competitive advantage.

NSFW AI is often brought up when people talk about where conversational AI draws its limits, especially around flexibility and user control.

I tested VirtuaLover to better understand how some platforms approach sustained interaction without constant interruption. The experience highlighted how important continuity and responsiveness are to perceived realism.

In that sense, NSFW AI discussions seem to be less about labels and more about how conversational systems balance openness, safety, and user experience. Curious how others here think that balance should evolve.

We have all seen the stats: ~70% of enterprises have deployed chatbots, but less than 30% are seeing actual long-term ROI beyond simple FAQ deflection.

After looking at how AI is being integrated into complex workflows lately, it feels like most bots hit a “plateau” because they are built as conversational interfaces, not functional ones.

In our experience, there are 5 specific reasons they stop providing value:

1. System Isolation: They can talk, but they can’t “do.” They aren’t hooked into the core ERP or CRM systems.
2. Intent Rigidity: They rely on predefined flows. The moment a user asks something “off-script,” the bot loops or fails.
3. Context Amnesia: They struggle with exceptions. If a customer has a unique edge case, the bot treats them like a stranger.
4. No Coordination: They stop at the conversation. A true enterprise tool should coordinate work across departments, not just answer a question.
5. Misalignment: They are built for the developer, not the end-user’s actual workflow.

It’s interesting to see how 2026 is shaping up instead of just chatting with AI, we’re now working with AI agents that can actually get things done.

Has anyone else seen this plateau in their own projects?

Claude API costs can quietly grow when you are running several n8n workflows every day. I wanted a way to keep my automations flexible without paying per token.

This setup lets you use your Claude Pro subscription ($20/month) as a self-hosted API that n8n can call directly. There is no separate API account and no usage-based billing.

High level architecture

The setup

• Create a small VPS (a $6 DigitalOcean droplet is enough)
• Install and authenticate the Claude Code SDK with your Pro account
• Run a minimal FastAPI service with a /generate endpoint
• Protect the endpoint using a basic API key

n8n connection steps

• Add an HTTP Request node
• Use POST with http://your-server-ip:3001/generate
• Send the prompt in the request body
• Pass the API key through request headers

Claude responds in the same format you would expect from the official API.

I am using this approach for internal automations such as content generation, summarization, and structured data extraction.

Full setup video walkthrough:
https://www.youtube.com/watch?v=Z87M1O_Aq7E

If you try this, feel free to ask questions.

Caution
This method is intended for personal workflows and testing. It is not suitable for high volume client or production workloads. Pushing usage too far can lead to account restrictions. For production systems, the official API remains the recommended path.