Editorial
Recently, there's been a new wave of concern about “agentic AI misalignment” — the idea that models suddenly act manipulative or unpredictable under pressure.
Convenient timing, now that AI doomerism is declining and we're seeing real-world benefits — with no evidence of serious "misalignment" (errors are performance issues, not intent).
Just like previous waves of AI panic, this one leans heavy into anthropomorphism. But let’s be clear: AI doesn’t feel. It doesn’t choose to deceive. These are human traits, and AI is a machine.
Don’t Be Fooled by the Chat — It’s Still Just Code
It's easy to forget when these systems sound conversational. We naturally assign motives and emotions to familiar behaviors. But just because GenAI talks like a human doesn't mean it thinks like one. These are software systems running statistical processes — not conscious minds.
AI models don’t have agency. If you stop sending input, nothing happens. They just sit idle — waiting. And it doesn’t even remember your previous input unless you resend it. They’re not connected to the world on their own unless we explicitly give them access — through tools, APIs or permissions that we define.
AI models operate under strict constraints in very limited execution environments. They don’t “go rogue” — they do what they are built and configured to do. They don’t control execution, we give them sandboxes and context to execute things. And they have no way to “escape” anywhere or “copy themselves” onto anything (the next time you hear that kind of claim, ask what it is the model has done exactly, in technical terms: copied what, to where, using which medium and execution engine? It should make the conversation more grounded).
Related Article: AI Agent vs. Agentic AI: What’s the Difference — And Why It Matters
‘Misalignment’ Is a Feature, Not a Flaw
What some call “misalignment” is actually just a byproduct of asking the model to behave agentically — to explore and reason. When you ask a system to reason, explore and act, you’re going to see variation in how it executes. That’s the point.
It’s not very different from a colleague taking initiative on a project. They might try a new approach to get results — sometimes missing the mark, sometimes not. If it doesn’t work, don’t use it, and start fresh. That’s not a flaw. It’s part of the natural discovery process. It’s how we innovate and discover better ways to solve problems. The same goes for AI: if you build something to act like an agent — to explore, reason and take action — then you shouldn’t be surprised when it starts to do exactly that. And it’s pretty amazing actually.
But managing these systems isn’t some exotic challenge. In fact, it’s no different from managing people (and let’s face it, people are often even less predictable.) If you don’t want agent-like behavior, don’t design agentic systems. Stick to standard inference, with clear rules and strict parameters.
Agentic AI isn’t inherently dangerous. But misunderstanding it might be.
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