What Is an “Agent,” Anyway? (And Why Your Marketing Team Should Know)

By Sumit Bhagchandani

Every leadership meeting I’ve sat in over the past year has featured some version of the same sentence: “We’re going agentic.” It’s said with total confidence, usually right before the slide changes and nobody explains what it actually means. I’ve started calling this the Boardroom Buzzword Test: say “AI agent” out loud in a meeting, watch heads nod, then ask “okay, but what does it do?” and watch the room go quiet like you just asked who forgot to refill the coffee.

I don’t blame anyone. “Agent” has become marketing’s favorite empty calorie, technically a word that doesn’t really nourish anything. So let’s fix that, because if you’re building campaigns, managing a martech stack, or just trying to survive your company’s AI strategy offsite, you need to know the difference between a real agent and a chatbot wearing a name tag.

Here’s the one-sentence version: an agent is a system that can plan, act, and adapt, without a human clicking “approve” at every single step. That’s it. That’s the whole club membership requirement.

Which means most of what gets called “agentic AI” in vendor decks doesn’t actually qualify. A few examples of things that are absolutely not agents, no matter how impressive the demo looked:

• A custom GPT. Text goes in, text comes out. It has opinions, not hands. It can’t do anything on its own.
• ChatGPT or Claude by themselves. Brilliant reasoning, zero execution. Think of them as the smartest person in the room who isn’t allowed to touch the keyboard.
• Zapier plus an LLM. Impressive, useful, and still just a very well-dressed set of if-this-then-that rules. An agent makes a decision. A workflow follows one.

So what does qualify? I think about it as six zones a real agent has to move through, on a loop, mostly unsupervised:

1. Perception: It scans its environment: data, tools, system states.
2. Memory: It retains context instead of relearning the same thing every Tuesday.
3. Reasoning: It breaks a goal into steps and figures out a path.
4. Action: It actually calls the API, updates the system, moves the needle. Skip this step, and you’ve built a very articulate chatbot.
5. Feedback: It watches what happened, adjusts, retries, or knows when to bring in a human.
6. Collaboration: It hands off to another agent, or to a person, when the job calls for it.

Perception, memory, reasoning, action, feedback, collaboration- that loop is the actual dividing line between “agent” and “script with a nice interface.” 

Few examples of what an Agent is 

• Claude Cowork handling a research-to-report workflow; you hand it a task like “pull last quarter’s campaign data and build me a summary,” and it decides which tools to use (web, files, spreadsheets), works through the steps on its own, and hands you back a finished document, instead of you manually stitching together five different app windows.
  
• An n8n workflow with an AI agent node monitoring inbound leads, it reads a new lead, decides whether it’s qualified, chooses which CRM fields to update, and routes it to the right follow-up sequence, versus a traditional n8n workflow where every branch and condition has to be pre-built by a human.

Here’s why this matters far beyond the engineering team: the numbers tell an uncomfortable story. Roughly 85% of enterprises say they want to be “agentic” within two to three years (Celonis, 2026 survey). Only 23% are actually scaling agents today, while another 39% are stuck in what I call pilot purgatory — forever in proof of concept, never in production (McKinsey, The State of AI in 2025). And a meaningful share of those projects are expected to fail, not because the technology broke, but because nobody agreed on the definition before they started building.

That’s the part that should worry marketers in particular, because we’re the ones being handed the budget line and the buzzword in the same breath. Picture the campaign version of this: your team says an “agent” will personalize email sends based on real-time behavior. Great, does it actually decide when and what to send on its own, learning from opens and unsubscribes as it goes? Or does it just run the same segmentation rules you built in 2019, dressed up with a new label? One of those is a genuine step-change in how your marketing operates. The other is a rebrand. Both are pitched to leadership as “AI agents,” but only one is worth the investment.

“What are you calling an ‘agent’ at your company that might actually just be a workflow?”

The fix is the same one-question litmus test I use in every meeting now: does the system decide what to do next, or does it wait to be told? If it waits, it’s a tool, a genuinely useful one, worth building and budgeting for; just don’t call it something it isn’t. If it decides, that’s an agent, and it deserves a very different level of scrutiny, governance, and expectation-setting before it touches your customer data or your brand voice.

So next time someone on your team or in a vendor pitch claims agentic AI capability, don’t nod along. 

Ask: 

• What decides 
• What remembers,
• What happens when it’s wrong. 

The answer will tell you everything about whether you’re looking at the future of your marketing stack or just a very confident chatbot with a better outfit.

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