The Agency Illusion: Why We’re Building AI Backwards

Every conversation about AI agency starts with the same flawed assumption: that intelligence lives inside individual agents. We’re obsessed with building autonomous systems that can think, decide, and act independently. But this entire framing misses something fundamental about how intelligence actually works.

The real insight isn’t about making agents more autonomous. It’s about making them more contextual.

The Myth of Individual Intelligence

When we talk about AI agents, we imagine discrete entities with their own intelligence, like digital employees who can handle tasks independently. This seems obvious—after all, you have your own thoughts, your own decision-making process, your own intelligence. But this intuition leads us astray.

Consider how you actually make decisions. When you’re solving a complex problem at work, you don’t just rely on what’s in your head. You draw on conversations with colleagues, frameworks from your industry, cultural norms, organizational processes, and accumulated knowledge from countless sources. Your “individual” intelligence is actually you tapping into and borrowing from collective intelligence.

The smartest person in the room isn’t the one with the highest IQ—it’s the one who can best access and synthesize the collective intelligence around them. They know who to ask, what frameworks to apply, which precedents matter, and how to combine insights from different domains.

This isn’t just about having good sources. It’s about recognizing that intelligence itself is fundamentally a group property that individuals participate in, not something that exists in isolation.

Intelligence as a Borrowing System

Think about language. You don’t personally invent the words you use or the grammatical structures that make communication possible. You borrow them from the linguistic system that surrounds you. Your ability to think complex thoughts depends on concepts, frameworks, and reasoning patterns that were developed collectively over centuries.

The same pattern applies to professional expertise. A doctor’s diagnostic ability isn’t just personal knowledge—it’s their capacity to access and apply the collective intelligence of medical science, institutional protocols, peer networks, and accumulated case studies. Remove them from that context, and their individual intelligence becomes much less capable.

This reveals something important: intelligence isn’t compositional in the way we usually think. We can’t break it down into atomic units of individual intelligence that combine to create group intelligence. Instead, it’s the group property that comes first, and individuals become intelligent by participating in it.

But here’s where it gets interesting. Intelligence is compositional, just in a different way than we assumed. As you decompose intelligent systems into smaller parts, each part becomes less intelligent. It’s compositional but continuous—there’s no fundamental “atom of intelligence” that you reach by breaking things down further.

The Continuous Decomposition Problem

This creates a puzzle for how we build AI systems. If you take an intelligent organization and try to automate one piece of it, that piece becomes less intelligent when isolated. The same task that works well within a larger system fails when you try to make it autonomous.

We see this constantly in AI deployments. A chatbot that seemed smart in demos becomes frustratingly limited in real use. An automated system that worked well in controlled tests breaks down when it encounters the messy reality of actual business processes. The problem isn’t that the technology is bad—it’s that we’re trying to make isolated pieces intelligent instead of helping them participate in collective intelligence.

This is why context matters more than autonomy. An AI system that can effectively tap into and contribute to the collective intelligence of an organization will outperform one that tries to be completely self-sufficient, even if the autonomous system has more raw capabilities.

Why Contextual Intelligence Wins

Contextual intelligence means building systems that get smarter by better integrating with their environment, not by becoming more independent from it. Instead of asking “How can we make this agent more autonomous?” we should ask “How can we make this agent more contextually aware and connected?”

This changes everything about how we approach AI development. Instead of trying to pack more capabilities into individual agents, we focus on making them better at understanding their situation, accessing relevant information, and contributing to collective decision-making processes.

A contextually intelligent system knows when it’s out of its depth and needs human input. It understands the broader goals and constraints of the organization it’s operating within. It can recognize when a situation requires fresh thinking versus when it should follow established patterns.

Most importantly, it makes the humans around it more intelligent, rather than trying to replace them.

The Business Implications

This framework has immediate practical implications for how companies should approach AI implementation. Instead of looking for tasks that can be completely automated, look for ways AI can enhance the collective intelligence of your teams.

The most successful AI deployments aren’t replacing human decision-makers—they’re augmenting human intelligence by providing better context, surfacing relevant information, and helping people access the collective knowledge of the organization more effectively.

Consider customer service. The old approach tries to build an AI agent that can handle customer inquiries independently. The new approach builds AI that makes human customer service representatives more effective by giving them instant access to relevant information, suggesting response options based on similar past cases, and helping them understand the broader context of each customer interaction.

The AI doesn’t need to be autonomous. It needs to be contextually intelligent.

Rethinking AI Development

This shift in perspective changes how we should build AI systems. Instead of focusing on making individual models more capable, we should focus on making them better at participating in collective intelligence.

This means designing AI systems that can explain their reasoning, ask for clarification when needed, and contribute to collaborative decision-making processes. It means building systems that get smarter by integrating with existing knowledge systems, not by trying to recreate that knowledge internally.

It also means recognizing that the most valuable AI systems won’t be the ones that can work completely independently, but the ones that can most effectively amplify and connect human intelligence.

The goal isn’t to build AI that thinks like humans, but AI that thinks with humans.

The Path Forward

The current obsession with autonomous agents is leading us down a expensive and ultimately limiting path. We’re trying to solve the wrong problem. Instead of asking how to make AI agents more independent, we should be asking how to make them more contextually integrated.

This doesn’t mean abandoning the goal of capable AI systems. It means recognizing that the most capable systems will be those that can effectively participate in and enhance collective intelligence, not those that try to replace it.

The companies that understand this will build AI systems that make their organizations genuinely more intelligent. The ones that don’t will be stuck with expensive autonomous systems that can’t quite handle the complexity of real-world situations.

The conversation around AI agency isn’t just backwards—it’s preventing us from building the kind of intelligent systems that could actually transform how we work. The future of AI isn’t about creating digital employees. It’s about creating digital collaborators that make human intelligence more powerful and more connected.

The question isn’t whether your AI can work alone. The question is whether it can make your team smarter together.