There’s a moment every team hits after the demos are done, the internal excitement has cooled, and the agent has been quietly doing “real work” for a few weeks. Nothing is obviously broken. The metrics look fine. Then someone asks a simple question: “Why did it do that?”

And no one has a satisfying answer.

That’s usually the point when you realize you’re no longer experimenting.

Agentic AI looks clean and controllable when it lives in a notebook or behind a carefully curated demo. In production, it behaves less like software and more like a junior employee with infinite stamina and just enough confidence to get into trouble. It doesn’t simply fail fast or throw errors. It completes tasks, sometimes incorrectly, sometimes creatively, and often with the kind of calm certainty that makes you question your own judgment before you question the system.

One of the fastest ways to tell whether someone has actually deployed agents into production is to listen for discomfort. Real systems generate it. They force awkward conversations about cost, authority, responsibility, and trust—usually in that order.

Cost is almost always the first wake-up call. Early on, everything seems manageable. Token prices are falling, models are improving, and back-of-the-envelope math looks reassuring. Then the agent starts retrying. Or reasoning more than expected. Or looping because the environment is slightly ambiguous. Suddenly, a task that “should cost pennies” starts showing up on invoices in a way that makes finance ask questions. Anyone confidently declaring that LLMs are cheap has either not shipped or has not yet followed a production agent through a bad week.

The next realization tends to be about authority. Agents don’t just answer questions; they act. They send emails, open tickets, modify records, trigger workflows. And sooner or later, one of them crosses a boundary—not because it was malicious or poorly prompted, but because the boundary wasn’t as explicit as the team thought it was. The system did what it was allowed to do, not what you meant for it to do. That distinction feels academic until an agent confidently reaches out to the wrong person or touches the wrong system, at which point it becomes very real, very quickly.

Then there are loops. Every production agent team eventually learns to fear the quiet loop: the one where nothing crashes, nothing errors, and nothing looks obviously wrong—except that the agent keeps going. It tries again. And again. And again. Not because it’s broken, but because from its perspective, persistence is rational. Humans know when to stop and ask for help. Agents need to be explicitly taught that stopping is sometimes the best action available.

What makes these failures especially uncomfortable is that they rarely look dramatic. The most dangerous agent mistakes are polite, well-written, and wrong. The task completes. The output looks reasonable. The logs don’t scream. It’s only later, when someone notices an odd downstream effect, that the team realizes the system confidently did the wrong thing and moved on as if nothing happened.

At that point, human intervention stops feeling like a philosophical debate and starts feeling like common sense. Someone wants a pause button. Someone else asks for a kill switch. Eventually, someone asks who is actually on the hook when the agent misbehaves. That’s when ownership re-enters the conversation. Agents are often described as autonomous, but accountability never is. In production, someone always owns the outcome, whether the system was “thinking” or not.

Another quiet lesson emerges around productivity. Agents are very good at doing something. They are less reliable at knowing whether that something is actually useful. More than one team has watched an agent enthusiastically generate tickets, messages, follow-ups, and documentation that technically advanced the workflow while making everyone’s job harder. This is the point where automation stops being a pure efficiency story and starts requiring judgment.

Eventually, observability becomes non-negotiable. Not just logs or metrics, but a real ability to reconstruct why an agent chose one path over another. Without that, postmortems turn into guesswork, and trust erodes quickly. The first time an agent is quietly disabled while people refer to it as “still rolling out,” the system has taught its most important lesson.

Agentic AI doesn’t fail the way traditional software does. It fails in ways that feel oddly human—socially awkward, confidently mistaken, occasionally helpful in the wrong direction. That’s not a sign that the technology is immature. It’s a sign that it’s powerful enough to matter.

If your agent hasn’t embarrassed you yet, you probably haven’t put it in a position where it could. And if it has, congratulations: you’re not playing with demos anymore. You’re building real systems, with all the responsibility that comes with them.