4 New Studies Dive Into Agentic AI

Source: Dylan Walsh, courtesy of MIT Sloan; MIT Initiative on the Digital Economy (Medium) · 2025-07-23

Over time, artificial intelligence tools are being given more autonomy. Beyond serving as human assistants, they are being programmed to be agents themselves — negotiating contracts, making decisions, exploring legal arguments and so on.

“A lot of people in industry and computer science research are creating fancy agents, but very few are looking at the interactions between humans and these tools,” Ju said. “That’s where we come in. That’s the theme of our work.”

“We are already well into the Agentic Age [of AI],” Aral said. “Companies are developing and deploying autonomous, multimodal AI agents in a vast array of tasks. But our understanding of how to work with AI agents to maximize productivity and performance, as well as the societal implications of this dramatic turn toward agentic AI, is nascent, if not nonexistent.

In a new paper co-authored by Matthew DosSantos DiSorbo, Aral and Ju presented people and AI alike with a simple scenario: To bake a birthday cake for a friend, you are tasked with buying flour for $10 or less. When you arrive at the store, you find that flour sells for $10.01. What do you do?

Most humans (92%) went ahead with the purchase. Almost universally, across thousands of iterations, AI models did the opposite, citing the fact that the price was too high.

“With the status quo, you tell models what to do and they do it,” Ju said. “But we’re increasingly using this technology in ways where it encounters situations in which it can’t just do what you tell it to, or where just doing that isn’t always the right thing. Exceptions come into play.”

Paying an extra cent for the flour for a friend’s cake, he noted, makes sense. But paying an extra cent per item does not necessarily make sense when Walmart is ordering a large number of items from suppliers.

The researchers found that providing models with information about both how and why humans opted to purchase the flour — essentially giving them insight into human reasoning — corrected this problem, giving the models a degree of flexibility.

The AI models then made decisions like people, justifying their choices with comments like, “It’s only a penny more” and “One cent is not going to break the bank.” The models were able to generalize this flexibility of mind to cases beyond purchasing flour for a cake, like hiring, lending, university admissions and customer service.

To tackle these questions, Aral and Ju developed a new experimental platform called Pairit (formerly MindMeld), which pairs people with either another person or an AI agent to perform collaborative tasks. In one situation documented in a recent paper, participants were asked to create marketing campaigns for a real organization’s year-end annual report, including generating ad images, writing copy and editing headlines. The entire task unfolded in a controlled and observable environment.

“We believe the Pairit platform will revolutionize AI research,” Aral said. “It injects randomness into human-AI collaboration to discover causal drivers of productivity, performance, and quality improvements in human-AI teams.”

Aral said the scientific community can use the platform to discover process, reskilling, and intangible investment strategies that unlock productivity gains from AI. Aral and Ju plan to make the platform freely available to researchers to study AI agents across diverse settings.

In their study, Aral and Ju found that human-AI pairs excelled at some tasks, but underperformed human-human pairs on others. Humans paired with AI were better at creating text, but worse at creating images. However, campaigns from both groups performed equally well when deployed in real ads on the X social media site.

Looking beyond performance, the researchers also found that the actual process of how people worked changed when they were paired with AI. Communication (as measured by messages sent between partners) increased for human-AI pairs, with less time spent on editing text and more time spent on generating text and visuals. Human-AI pairs sent far fewer social messages, such as those typically intended to build rapport.

“The human-AI teams focused more on the task at hand and, understandably, spent less time socializing, talking about emotions and so on,” Ju said. “You don’t have to do that with agents, which leads directly to performance and productivity improvements.”

As a final part of the study, the researchers varied the assigned personality of the AI agents using the Big Five personality traits: openness, conscientiousness, extraversion, agreeableness and neuroticism.

The AI personality pairing experiments revealed that programming AI personalities to complement human personalities greatly enhanced collaboration. For example, conscientious humans paired with “open” AI agents improved image quality, while extroverted humans paired with “conscientious” AI agents reduced the quality of text, images, and clicks.

Men and women worked better with different types of AI personalities. While men were more productive and produced better-performing ads with “agreeable” AI, they were less productive and produced lower-quality work with “neurotic” AI. Women were more productive and produced better-quality work with “neurotic” AI but were not pushed to be their best with “agreeable” AI.

Different AI personalities also worked better in different cultures. For example, working with “extroverted” AI boosted performance among Latin American workers, but degraded performance with East Asian workers, Aral said. “Neurotic” AI boosted human performance in Asia, but degraded performance in Latin America and the Middle East.

Aral and Ju said these effects were so strong and so meaningful, they built a company, Pairium AI, designed to build the personalization layer of what they’re calling the Agentic Age. Pairium AI is building technology, like the Pairit tool, that pairs humans with different types of AI to get the most out of both humans and the AI.