Why AI Agents Must Account for Human Behavior and Individual Differences

AI agents are taking an increasingly active role within organizations. Where AI was long used primarily for analysis and making predictions, we are now seeing systems that independently formulate recommendations, make decisions, and interact directly with people. It is precisely in this shift from analysis to action that a fundamental problem becomes visible: many AI agents are designed as if they operate in a world where people react more or less the same to the same stimuli. That world does not exist.

To understand why this is so problematic, we must first consider the nature of human behavior itself.

Human behavior cannot be modeled deterministically

Human behavior can be modeled, but it can never be fully captured in fixed rules. In economics and data science, we attempt to explain choices by looking at factors such as price, convenience, risk, time pressure, or social norms. This provides valuable insights, but it implicitly assumes that people make similar trade-offs.

In practice, however, people assign different weights to the same factors. What is decisive for one person may play hardly any role for another. Two individuals with the same information and in the same context can therefore arrive at completely different choices without either acting irrationally.

This variation in behavior is not an exception, but the norm. And exactly there, the need arises for a deeper understanding of the differences between people.

Unobserved heterogeneity: differences you don't see, but which drive behavior

In data and models, we only see a portion of what influences choices. Many individual drivers remain out of view, simply because they are difficult to measure or change per situation. These include personal values, previous experiences, risk attitude, trust, or emotions that do not fit neatly into a dataset.

In statistics, we call this unobserved heterogeneity: systematic individual differences that influence behavior but are not directly observable. Modern models explicitly recognize this by not assuming a single decision-making process, but a distribution of preferences within a population. The model does not know exactly who has which preference, but it does know that those differences exist.

That insight is crucial because as soon as we ignore this heterogeneity, we misinterpret behavior.

What goes wrong when AI agents assume averages

Many AI agents do exactly that: they abstract away differences. They optimize a single strategy, learn from average patterns, and implicitly assume that there is one best action for a given situation. As long as variation between users is limited, this appears to work.

But as soon as agents work with real people, friction arises. Recommendations that are valuable to one user encounter resistance from another. Nudges that help one person feel intrusive to another. Optimizations that seem efficient in the short term undermine trust and acceptance in the longer term.

What is often seen as noise or an exception is, in reality, the manifestation of underlying differences between people.

AI agents operate under structural uncertainty

For an AI agent, this heterogeneity is difficult because, by definition, the agent works with incomplete information. An agent never fully knows what someone's preferences are, what trade-off someone is making at that moment, or how stable those preferences are. Yet, the agent must act and make choices.

This means that AI agents must not only optimize but also continuously learn. Not after the fact, but during interaction. Feedback and deviating behavior are not errors in this context, but signals about preferences that were not yet explicitly known.

This reality requires a different way of thinking about intelligence.

From average optimization to individual fit

The central question for AI agents is therefore no longer what works best on average, but for whom something works, in what context, and why. Successful AI agents do not just predict behavior; they actively learn who they are facing and adjust their decisions accordingly.

This means that personalization is not an extra layer added later, but a fundamental part of the decision-making process itself. The agent must be able to maintain different hypotheses about users and be prepared to adjust those hypotheses based on new information.

Unobserved heterogeneity as a design principle for AI agents

What began in statistics as a correction for invisible differences becomes a strategic design principle in the context of AI agents. Unobserved heterogeneity means acknowledging that not everything is measurable, but that systems can learn to handle uncertainty and variation.

AI agents designed for this make better decisions, are more robust in complex environments, and build more sustainable trust in interactions with humans.

Conclusion

The future of AI agents lies not in even more data or more complex models alone. It lies in human-centered design. Not by abstracting away human differences, but by putting them at the center. Not by searching for the perfect average action, but by learning to handle human diversity. Ultimately, that is where intelligent AI distinguishes itself.

‍

// About the author

Job van den Berg

Mede-oprichter, AI Keynote Spreker & Techondernemer

Tech-ondernemer (1989) met een achtergrond als socioloog (Research Master (MSc) in statistiek en sociologie) en een van de meest gevraagde keynote sprekers over AI en data in Nederland. Als mede-oprichter van Ai.nl, The Automation Group en Proxies leidt hij engineers die agentic AI van prototype naar productie brengen binnen enterprises. Op het podium vertaalt Job die hands-on praktijk naar concrete strategieën. Eerder was Job Chief Data bij o.a. DPG Media en Kantar. Hij is co-auteur van 5 boeken over AI waaronder 'AI Agents' en 'Handboek AI Strategie' en een veelgevraagd expert in de landelijke media.

LinkedIn