Agentic AI: Winning in a World That Doesn't Work That Way

Agentic AI is being trained to “win.” But human systems aren’t games—they’re stories. The consequences of confusing the two will define the next decade.

When machines are trained to win, they inherit our strategy but not our story. Agentic AI doesn’t ... More fail because it lacks intelligence—it fails because it lacks context.

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The Real Mistake Is in the Rules We Teach Agentic AI

Agentic AI is being built on the assumption that the world is a game—one where every decision can be parsed into players, strategies, outcomes, and a final payoff.

This isn’t a metaphor. It’s code.

In multi-agent reinforcement learning (MARL), agents use Q-functions to estimate the value of actions in a given state to converge toward an optimal policy. MARL underpins many of today’s agentic systems.

A Q-function is a mathematical model that tells an AI agent how valuable a particular action is in a given context—essentially, it’s a way of learning what to do and when to maximize long-term reward. But “optimal” depends entirely on the game’s structure—what’s rewarded and penalized and what constitutes “success.” Q-learning becomes a hall of mirrors when the world isn’t a game. Optimization spirals into exploitation. MARL becomes even more hazardous because agents must not only learn their policies but also anticipate the strategies of others, often in adversarial or rapidly shifting contexts, as seen in systems like OpenAI Five or AlphaStar.

At the heart of agentic AI—AI designed to act autonomously—is a set of training systems built on game theory: multi-agent reinforcement learning, adversarial modeling, and competitive optimization. While tools like ChatGPT generate content based on probability and pattern-matching, agentic AI systems are being built to make autonomous decisions and pursue goals—a shift that dramatically raises both potential and risk.

Why Agentic AI Will Soon Make ChatGPT Look Like A Simple Calculator outlines just how different—and disruptive—this new generation of AI will be. The machine learns to act by maximizing rewards inside a simulated environment governed by rules and outcomes.

The problem is that human life doesn’t (and more importantly, shouldn’t be induced to) work that way. Game theory is a powerful tool for analyzing structured interactions, such as poker, price wars, and Cold War standoffs.

Those are not games. They are stories. And storytelling isn’t ornamental—it’s structural. We are, as many have argued, not just homo sapiens but homo narrans: the storytelling species. Through narrative, we encode memory, make meaning, extend trust, and shape identity. Stories aren’t how we escape uncertainty—they’re how we navigate it. They are the bridge between information and action, between fact and value.

To train machines to optimize for narrow wins inside rigid systems is to ignore the central mechanism by which humans survive uncertainty: We don’t game the future—we narrate our way through it.

And training agents to “win” in an environment with no final state isn’t just shortsighted—it’s dangerous.

Game Theory and the Lie That Trains Agentic AI

Game theory assumes a closed loop:

• Known players
• Defined actions
• Predictable payoffs
• An end state

Simon Sinek famously argued that business is an “infinite game.” But agentic AI doesn’t play infinite games—it optimizes finite simulations. The result is a system with power and speed, but lacking intuition for context collapse. Even John Nash, the father of equilibrium theory, understood its fragility. His later work acknowledged that real-life decision-making is warped by psychology, asymmetry, and noise. We’ve ignored that nuance.

But in real life—especially in business—the players change, the rules mutate, and the payoffs are subjective. Even worse, the goals themselves evolve mid-game.

In AI development, reinforcement learning doesn’t account for that. It doesn’t handle shifting values. It handles reward functions. So, we get agents trained to pursue narrow, static goals in an inherently fluid and relational environment. That’s how you get emergent failures—agents exploiting loopholes, corrupting signals, or spiraling into self-reinforcing error loops.

We’re not teaching AI to think.

We’re teaching it to compete in a hallucinated tournament.

Humans Don’t Play to Win—We Play to Matter

This is the crux: humans are not rational players in closed systems.

We don’t maximize. We mythologize.

Evolution doesn’t optimize like machines do—it tolerates failure, ambiguity, and irrationality as long as the species endures. It is selected not just for survival and cooperation but also for story-making because narrative is how humans make sense of uncertainty. People don’t start companies or empires solely to “win.” We often do it to be remembered. We blow up careers to protect pride. We endure pain to fulfill prophecy. These are not strategies—they’re spiritual motivations. And they’re either illegible or invisible to machine learning systems that see the world as a closed loop of inputs and rewards.

We pursue status, signal loyalty, perform identity, and court ruin—sometimes on purpose.

You can simulate “greed” or “dominance” by tweaking rewards, but these are surface-level proxies. As Stuart Russell notes, the appearance of intent is not intent. Machines do not want—they merely weigh.

When agents start interacting under misaligned or rigid utility functions, the system doesn’t stabilize. It fractures. Inter-agent error cascades, opaque communications, and emerging instability are the hallmarks of agents trying to navigate a reality they were never built to understand.

What Agentic AI Misses in the Exam Room

Imagine a patient sitting across from a doctor with a series of ambiguous symptoms—fatigue, brain fog, and minor chest pain. The patient has a family history of heart disease, but their test results are technically “within range.” Nothing triggers a hard diagnostic threshold. An AI assistant, trained on thousands of cases and reward-optimized for diagnostic accuracy, might suggest no immediate intervention—maybe recommend sleep, hydration, and follow-up in six months.

The physician, though, hesitates. Not because of data but because of tone, posture, and eye contact, because the patient reminds them of someone, because something feels off, even though it doesn’t compute.

So, the doctor ordered the CT scan against the algorithm’s advice. They find the early-stage arterial blockage. They save the patient’s life.

Why did the doctor do it? Not because the model predicted it. Because humans don’t operate on probability alone—we act on a hunch, harm avoidance, pattern distortion, and story. We’re trained not only to optimize for outcomes but also to prevent regret.

A system trained to “win” would have scored itself ideally. It followed the rules. But perfect logic in an imperfect world doesn’t make you intelligent—it makes you brittle.

The Metaphor That Misguides Agentic AI

The fundamental flaw in agentic AI isn’t technical—it’s conceptual. It’s not that the systems don’t work; they’re working with the wrong metaphor.

We didn’t build these agents to think. We built them to play. We didn’t build agents for reality. We built them for legibility.

Game theory became the scaffolding because it provided a structure, offering bounded rules, rational actors, and defined outcomes. It gave engineers something clean to optimize. But intelligence doesn’t emerge from structure; it arises from adaptation within disorder.

The gamification of our informational matrix isn’t neutral. It’s an ideological architecture that recodes ambiguity as inefficiency and remaps agency into pre-scored behavior. This isn’t just a technical concern—it’s an ethical one. As AI systems embed values through design, the question becomes: whose values?

The Ethics of AI: Balancing Innovation with Responsibility explores this tension between progress and principle, and why getting it wrong has real-world consequences. It’s not that these systems are “wrong,” it’s that the vehicle values most of us wouldn’t consent to if made explicit. Optimization is not the truth. It’s perspective, smuggled in as precision.

In the wild, intelligence isn’t about winning. It’s about not disappearing. It’s about adjusting your behavior when the ground shifts under you because it will. No perfect endgames exist in nature, business, politics, and human relationships; they are just survivable next steps.

Agentic AI, trained on games, expects clarity. But the world doesn’t offer clarity. It offers pressure. And pressure doesn’t reward precision—it rewards persistence.

This is the failure point. We’re asking machines to act intelligently inside a metaphor never built to explain real life. We simulate cognition in a sandbox while the storm rages outside its walls.

If we want beneficial machines, we need to abandon the myth of the game and embrace the truth of the environment: open systems, shifting players, evolving values. Intelligence isn’t about control. It’s about adjustment, not the ability to dominate, but the ability to remain.

The Real Frontier: Mechanized Labor vs. Agentic AI

While we continue to build synthetic minds to win fictional games, the actual value surfaces elsewhere: in machines that don’t need to want. They need to move.

Mechanized labor—autonomous systems in logistics, agriculture, manufacturing, and defense—isn’t trying to win anything. It’s trying to function. To survive conditions. To optimize inputs into physical output. There’s no illusion of consciousness—just a cold, perfect feedback loop between action and outcome.

Unlike synthetic cognition, mechanized labor solves problems the market understands: how to scale without hiring, operate in unstable environments, and cut carbon and cost simultaneously. Companies like John Deere are already deploying autonomous tractors that don’t need roads or road signs. Amazon has doubled its robotics fleet in three years. These machines aren’t trying to win. They’re trying not to break.

And that’s why capital is quietly pouring into it.

• Labor is scarce.
• Energy is expensive.
• The edge is smart.
• The returns are real.

The next trillion-dollar boom won’t be in artificial general intelligence. It’ll be in autonomous physicality. The platforms we think of as background are about to become intelligent actors in their own right. “We have become tools of our tools,” wrote Thoreau in “Walden” in 1854, just when the industrial revolution began to transform not just Concord, but America, Europe, and the world.

Intriguingly, Thoreau includes mortgage and rent as “modern tools” to which we voluntarily enslave ourselves. What Thoreau was pointing to with his experiment in the woods was how our infrastructure, the material conditions of our existence, comes to seem to us “natural” and inevitable, and that we may be sacrificing more than we realize to maintain that infrastructure. AI - intelligent, autonomous tools - represents a categorical shift in how we coexist with our infrastructure.

Infrastructure isn’t just how we move people, goods, and data. It’s no longer just pipes, power, and signals. It’s “thinking” now—processing, predicting, even deciding on our behalf. What was once physical has fused with the informational. The external world and our internal systems of meaning are no longer separate. That merger isn’t just technical—it’s existential. And the implications? We’re not ready.

But if AI is to become all of our closest, most intimate companions, we should be clear on what it is, exactly, that we have trained it, and allowed it, to do. This isn’t just logistics. It’s the emergence of an industrial nervous system. And it doesn’t need to ‘win.’ It needs to scale, persist, and adapt—without narrative.

Stop Teaching Agentic AI to Win. Teach It to Endure.

We’re building agentic AI to simulate our most performative instincts while ignoring our most fundamental one: persistence.

The world isn’t a game. It’s a fluid network of shifting players, incomplete information, and evolving values. To train machines as if it’s a fixed competition is to misunderstand the world and ourselves.

We are increasingly deputizing machines to answer questions we haven’t finished asking, shaping a world that feels more like croquet with the Queen of Hearts in Alice’s Adventures in Wonderland: a game rigged in advance, played for stakes we don’t fully understand.

If intelligence is defined by adaptability, not perfection, endurance becomes the ultimate metric. What persists shapes. What bends survives. We don’t need machines that solve perfect problems. We need machines that function under imperfect truths.

The future isn’t about agentic AI that beats us at games we made up. It’s about agentic AI that can operate in the parts of the world we barely understand—but still depend on.