How to Tell a Real AI Agent from a Rebranded Bot
OVERVIEW
- SUMMARY
The conversation discusses the term "AI agent" and the challenges in defining it clearly. They explore the key characteristics that distinguish a true autonomous AI agent from simpler automation tools, and provide a practical checklist for identifying genuine AI capabilities. They also discuss the risks of misunderstanding the term and the potential for greater clarity as real-world use cases and success stories emerge.
CRITICAL QUESTIONS POWERED BY PERIGON°
Ask Perigon 
What are some specific examples of real-world deployments of genuine autonomous AI agents that have delivered tangible benefits?
Autonomous AI agents are being deployed across various industries, offering tangible benefits such as increased efficiency, reduced costs, and improved decision-making. A genuine autonomous AI agent can proactively make decisions and complete tasks independently without constant human intervention. They can operate without waiting for instructions.
Here are some examples:
Cybersecurity: Exabeam Nova is an autonomous AI agent designed to accelerate incident response, reduce investigation times, and improve threat mitigation for security teams. It correlates attack signals, investigates cases, and classifies threats.
How do the technical capabilities and architectures of true AI agents differ from those of more advanced automation tools?
True AI agents possess distinct technical capabilities and architectures compared to advanced automation tools. AI agents are designed to perceive their environment, make decisions, and take actions to achieve specific goals autonomously. They incorporate reasoning, adaptability, and decision-making to achieve goals, whereas microservices, a form of advanced automation, require explicit instructions.
How might the emergence of genuine AI agents impact the future of work, decision-making, and problem-solving across different industries and domains?
The emergence of genuine AI agents is poised to significantly reshape the future of work, decision-making, and problem-solving across various industries. These agents, capable of autonomous reasoning, planning, and action, are expected to drive substantial changes in how businesses operate and how humans interact with technology.
TRANSCRIPT (FOR THE ROBOTS)
Aimee: You know that feeling when a word just sort of catches fire. Right Now for me, it feels like that word is AI agent.
Craig: Oh, definitely. It's everywhere.
Aimee: You know that feeling when a word just sort of catches fire. Right Now for me, it feels like that word is AI agent.
Craig: Oh, definitely. It's everywhere.
Aimee: Exactly. You see it in marketing, tech news, maybe even popping up in work meetings. But honestly, when you try to actually pin down what it means…
Craig: It gets really fuzzy fast.
Aimee: Yeah. The Wall Street Journal even pointed this out, didn't they? That there's just no single clear definition floating around.
Craig: And Prem Natarajan over at Capital One, he had a great analogy, he called it the “elephant in the room…”
Aimee: Right, because everyone's describing a different piece of it.
Craig: That's basically the problem. The term AI agent is. Well, it's becoming almost meaningless because it's stretched so thin.
Aimee: So you've got simple chatbots that just follow a script.
Craig: Or maybe some slightly fancier automation tools.
Aimee: Yeah.
Craig: They're all getting slapped with the AI agent label.
Aimee: And that makes it tough, especially for listeners, maybe making tech decisions, trying to figure out what's really capable.
Craig: Incredibly difficult. It obscures the real potential of genuine AI agents.
The enterprise software world is awash in AI “agents”—or at least, things being marketed as such. As CIOs, CAIOs, and tech leaders try to cut through the noise, one thing is becoming clear: we need better definitions. Right now, the term “AI agent” is being stretched to the point of meaninglessness, and that’s a problem.
Why the Confusion?
As WSJ recently noted, everyone from analysts to AI lab founders to Fortune 500 execs are grappling with the same question: What exactly makes something an agent?
Prem Natarajan, Capital One’s chief scientist, likened it to the “elephant in the room” parable—everyone’s touching a different part and describing something else entirely. And that’s exactly what’s happening in today’s market. A chatbot that schedules meetings? Agent. A script that pulls data and formats it for a report? Agent. A macro-powered automation tool dressed up in LLM clothes? Yep—still being called an agent.
But if everything is an agent… then nothing is.
How Perigon Thinks About AI Agents
Perigon VP of Product Zach Bartholomew recently weighed in with a simple but powerful framing:
“I think of an AI agent as software that can perceive its environment, plan out how to respond, and take meaningful action largely on its own. In other words, it’s not just following a rigid script or waiting for a person to hit ‘approve.’ True autonomy means it can learn and adapt to meet its goals in real time.”
This is the crux of the definition: autonomy. An AI agent isn’t just reactive. It’s proactive, goal-oriented, and capable of independent decision-making.
Why the Hype (and Misdirection)?
AI agents are undeniably hot right now. And in the scramble to capitalize on the buzz, a lot of vendors are labeling their products as “agentic,” even if they’re little more than glorified assistants. That’s marketing 101: slap the trendiest label on your existing product.
But this shortcut has consequences.
How to Spot a Real AI Agent
So how can leaders separate the real from the hype? Here are the hard questions Perigon recommends asking:
- Can it plan and execute multi-step processes on its own?
- Does it learn or improve over time?
- Can it take meaningful action without human approval?
- What kind of decisions can it make independently?
- How well does it integrate with your current stack?
If the answer to most of these is “no” or “sort of,” you’re probably not dealing with a true AI agent.
Where We’re Headed
The good news: clarity is coming. As real-world deployments increase and success stories emerge, the line between “chatbot” and “agent” will become more defined.
Bartholomew predicts:
“Over the next year or two, I expect we’ll see clearer definitions and real success stories that separate true AI agents—capable of unsupervised learning and autonomous action—from glorified chatbots or macro-based automation tools.”
In the meantime, CIOs and IT leaders should stay skeptical, ask hard questions, and look for solutions that actually deliver on the promise of intelligent autonomy.
Because betting on the wrong horse now? That’s a future disadvantage in the making.
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