Everybody Talks About AI Agents

But what are they, really — and why should we care?

A manager asks their team to prepare a comprehensive material about agentic AI.
The team uses AI to write it.
The manager then pastes it into another AI tool to summarize it into bullet points for the board.
Everyone feels efficient.
Nobody actually knows what agentic AI is.

That’s where we are with this topic.
It’s everywhere: in headlines, meetings, strategy decks. But ask ten people what an AI agent really is, and you’ll get twelve answers.

Here’s the simple version.
Agentic AI isn’t a smarter chatbot or another productivity plugin.
It’s AI that can act, not just react.
You don’t have to tell it what to do every time, you tell it what you want to achieve.
It can plan steps, learn from outcomes, and adapt along the way.
Think of it less like a tool on your desktop and more like a junior colleague who sometimes surprises you by doing things you didn’t ask for. Hopefully in a good way.

At some point soon, your AI might call the dentist to schedule an appointment.
The receptionist won’t pick up, because the dentist’s AI will answer the call, check the calendar, and confirm the slot automatically.
The humans will find out later, when the meeting reminder pops up.
Efficiency achieved. Understanding optional.

Agentic AI isn’t something to fear or worship.
It’s something to manage.

Tools that can act on their own need people who can define what “good” looks like.
That means setting better goals, clearer feedback, and boundaries that make sense.
In other words, leading this technology the way you’d lead a team: with direction, trust, and accountability.

As agentic systems begin to act in the real world, not just generate text, human-in-the-loop control becomes essential.
Someone has to stay responsible for accuracy, compliance, and fairness.
Humans don’t just correct mistakes; they close the feedback loop, helping the system learn safely and stay aligned with real-world intent.
That’s what keeps autonomy from turning into chaos.

But being “in the loop” isn’t as simple as it sounds.
It means being accountable for something you didn’t fully create, like signing off on a report you didn’t completely write.
You’re responsible not only for outcomes, but for the logic behind them, even when that logic is partly invisible.
You gave the AI a goal, it went off and did things, and now you’re supposed to explain why.
That’s leadership in the age of agentic AI. Good luck.

So the challenge isn’t to outsmart the machine. It’s to outframe it.
You don’t need to think faster than the model; you need to think wider, to hold context, purpose, and ethics together when the system only sees patterns.
That demands a new mix of skills: critical framing, interpretive judgment, and the ability to know when to trust automation and when to interrupt it.

Accountability with AI moves from making things to making sense of them.
From doing the work to defining what “good work” still means.

As filmmaker Guillermo del Toro said recently:

“I will rather die than use AI. I don’t want machines writing or designing what only humans should feel.”

He’s right to warn us.
But maybe the real answer isn’t less technology, it’s better supervision.
We don’t need to step out of the loop. We just need to know when to stay in, and how to manage it.

If you want to go deeper (and sound smart in your next meeting), read this piece from VentureBeat:
👉 We keep talking about AI agents — but do we ever know what they are?

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