I’ve spent half a century writing about field agents—men and women who operate in that shadow realm where a misread glance can mean death, where trust is a currency more valuable than any cryptocurrency, and where the difference between survival and catastrophe lives in the space between heartbeats. Lately, I’ve been reading articles that suggest agentic AI will make them obsolete. That machine learning can predict human behavior better than humans can. That algorithms will replace instinct.
They’re wrong.
In White Wolf, Evan Ryder confronts a world where digital walls no longer protect secrets, where unhackable communication systems threaten to topple governments. But here’s what the technologists miss: the technology isn’t the story. It never is. The story lives in that moment when Ryder reads a room—not the data streaming from it, but the imperceptible shift in a target’s stance, the fleeting look in their eye, the fraction-second hesitation before a lie, the smell of fear poorly masked by expensive cologne.
An agentic AI can process a billion data points. It can analyze facial micro-expressions with millisecond precision. It can predict behavioral patterns with staggering accuracy. What it cannot do—what it will never do—is know when the data is lying.
I’ve watched this play out across decades of creating intelligence operatives. The best field agents possess something that exists beyond the reach of any algorithm: a trained observer’s ability to sense wrongness. Not wrong information—wrongness itself. That electric charge in the air before an ambush. The too-perfect cover story. The asset who’s giving you everything you want to hear precisely because they’ve been turned.
An AI makes decisions based on probabilities. A field agent survives on certainties that can’t be quantified. When Ilona Shokova, the White Wolf, enters a room, she’s reading forty variables that no sensor array can capture—the quality of silence, the pattern of breath, the way light falls on a weapon someone’s trying to conceal. She’s processing not just what is, but what should be and isn’t.
The machine evangelists will tell you that AI eliminates human error. But human error isn’t the problem in intelligence work. Human error is often the only thing keeping you alive. The agent who disobeys the algorithm because something feels wrong. The operative who trusts gut instinct over the mission brief. The field agent who breaks protocol because the safe house that checked out on every digital metric feels like a trap.
Evan knows what fear looks like when it’s trying not to show. An AI can identify stress markers in a voice. A trained human can tell you why someone’s afraid, and whether that fear serves your purpose or threatens your life. The difference isn’t incremental. It’s existential.
The future is upon us. A future where secrets can’t hide behind digital walls, where everything can be hacked, where information flows like water. In that world, the human field agent becomes more vital, not less. Because when the machines can compromise any system, when agentic AI can infiltrate any network, the only intelligence that matters is the kind that is only observable, interpreted, and understood by the human agent.
The tension isn’t between human and machine. It’s between those who understand that intelligence is ultimately about intelligence—the human kind, refined by training, sharpened by experience, tempered by survival—and those who believe everything an agentic AI spits out.
