What is an AI Agent?

Everyone wants AI agents. Many enterprises have even started pilots with AI agents. Some of what’s being sold as “AI agents” is just legacy automation with a fresh coat of paint.

Across enterprise tech, “AI agent” is the term of the year. It’s showing up in product pitches, vendor brochures, and investment decisions. But when you ask, “What does this agent actually do?”—the answers vary wildly.

Some pass off smart chatbots as agents. Others say it's any system that can trigger a workflow. Still others insist it must make autonomous decisions. Meanwhile, a lot of old-school process automation is quietly being rebranded as “agentic AI.”

This confusion isn’t just academic. It risks slowing down adoption and misdirecting investments. It is important not just to ask what is an AI agent, but what also what value is it delivering?

"If something exhibits the key traits of a duck, it's likely a duck" is an idiom I like to reference - but unfortunately it doesn't fully work when evaluating AI agents. Instead I have started using a few handy pointers when thinking about agents:

+ Is the system making context-aware decisions? 

+ Can it take useful action without being micromanaged? 

+ Is it actually improving customer experience, reducing human time spent, or driving revenue?

If the answer is yes for these, then you’re on the right track—regardless of what label you attach to it.

As we explore agent use cases, let us stay grounded in these principles:

--Define success by outcomes, not technical jargon.

--Don’t be distracted by the assistive vs. autonomous debate—value can come from both.

--Align on what “value” means across product, engineering, and business teams.

--Don’t mistake process automation for intelligence. And don’t confuse complexity with impact.

If the AI system is making life better, faster, or smarter—then we are doing it right. If it isn’t, maybe it’s just a dressed-up macro with a new name.