Dartmouth Researchers Assess Agentic AI

The idea of an "AI agent" originated with John McCarthy, who also organized the seminal 1956 Dartmouth Summer Research Project on Artificial Intelligence, says Nikhil Singh, assistant professor of computer science and director of the Science and Art of Human-AI Systems lab.

Singh and other Dartmouth professors are working to better understand agentic AI systems and examine their limitations.

"You have to be cognizant of all the bad things that happen and be upfront about the risks. It is dangerous right now because it’s willy-nilly," says Eugene Santos Jr., the Sydney E. Junkins 1887 Professor of Engineering. He cites the example of popular chatbots that confidently provide responses even when they don't know the right answer.

For Santos, the fix is a matter of engineering discipline. "For any engineering system we build, we go to great lengths to understand reliability. The same should apply for AI," says Santos, who studies trust in AI, computational intent, and explainable AI.

Intent is key, he says, emphasizing that creators must understand what they build and provide guarantees and clarity about the capabilities of their products.

"Off-label drug use can be a good analogy. There are some great purposes for it, but the intent should be as clear and/ or unambiguous as possible, and there must be transparency and accountability," says Santos.

"Of course, all these remain fundamental challenges towards building trustworthy AI systems."

Santos works with engineers and psychologists as well as other disciplines to understand what trust looks like in human-AI collaborations and examines how figuring out what the system is incentivized to do can help to understand and influence its biases.

Even as these cautions mount, Dartmouth researchers are putting agents to work in everything from quantum labs to energy markets to healthcare.

Assistant Professor of Engineering Cong Chen presents her research on decision-making during a power outage at CERAWeek 2026 in Houston in March. (Courtesy of CERAWeek by S&P Global)

Assistant Professor of Engineering Cong Chen gave her AI agents a different role. Several, in fact. They modeled a cautious grandmother, a data-savvy graduate PhD student, and an emotionally driven actor, and Chen watched them decide electricity consumption and home battery backup power usage during a simulated power outage. The student continued selling power, while the grandmother and the actor chose to save backup power.

The agents serve as digital proxies for various energy customers, generating behavioral insights about how people will respond to changes in electricity pricing, energy policies, or renewable sources incentives, especially during rare events like outages.

These insights enable simpler, fairer market design, and support Chen's research for real-time pricing that eliminates market failure and incentive distortions in the electricity market. Chen was part of the Dartmouth delegation at the 2026 CERAWeek conference held this March in Houston.

Overall, the research suggests that AI agents are tools capable of genuine assistance, but ones that come with warning labels, calling for skepticism and adoption, critique and construction, in equal measure.