How agentic AI misses the point of human systems

Agentic AI is being developed with a fundamental conceptual error – treating human systems as games with winners rather than evolving stories with complex dynamics. This philosophical distinction will likely shape AI’s development trajectory in the coming years, as we recognize that real-world intelligence isn’t about optimization toward fixed endpoints, but adaptation within constantly changing environments.

The big picture: Current agentic AI systems are built on reinforcement learning and game theory foundations that frame intelligence as optimization toward winning conditions rather than adaptation to complex realities.

• Multi-agent reinforcement learning (MARL) systems use Q-functions to estimate action values, essentially teaching AI to maximize rewards through optimal policies.
• This approach fundamentally misunderstands human systems, which operate more like evolving narratives than static games with fixed rules and victory conditions.

Why this matters: The mismatch between how AI systems are designed to “win” and how human systems actually function could lead to significant unintended consequences as autonomous systems become more prevalent.

• Unlike tools like ChatGPT, agentic AI systems make autonomous decisions and independently pursue objectives.
• Teaching machines to optimize for narrow, static goals in fluid environments creates the potential for emergent failures when conditions inevitably change.

The human element: People make decisions based on narrative understanding, emotional context, and adaptive reasoning that pure optimization algorithms struggle to replicate.

• Human intelligence evolved not primarily for domination but for adaptation and persistence within complex social and environmental systems.
• The absence of these narrative-based decision frameworks in AI systems represents a critical blind spot in current development approaches.

Where we go from here: The most promising frontier for AI may not be artificial general intelligence but specialized autonomous systems focused on adaptation rather than optimization.

• Future development should shift toward creating machines that can operate effectively in complex, imperfect environments without requiring a predefined “winning” state.
• Key sectors for this approach include logistics, agriculture, manufacturing, and defense – areas where functioning and surviving in changing conditions matters more than abstract optimization.