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Q&A: What is agentic AI today, and what do we want it to be?

ai, AI (Artificial Intelligence), Algorithms, Artificial Intelligence, Computer Science and Artificial Intelligence Laboratory (CSAIL), Computer science and technology, data, Electrical engineering and computer science (EECS), Human-computer interaction, Interview, Machine Learning, MIT Schwarzman College of Computing, School of Engineering

The deployment of automated software systems called AI agents has recently exploded. A November 2025 report by MIT Sloan School of Management and Boston Consulting Group found that 35 percent of surveyed businesses had already deployed AI agents, while another 44 percent planned to implement agentic AI soon. To understand the fundamentals and potential impacts of these […]

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3 Questions: Beyond data-driven aesthetics

ai, AI (Artificial Intelligence), Algorithms, Alumni/ae, Architecture, Artificial Intelligence, Computer science and technology, data, Design, Exhibits, Interview, Machine Learning, School of Architecture and Planning, Special events and guest speakers

“Beyond Data-Driven Aesthetics,” by MIT Architecture alumnus and researcher Alexandros Haridis, on view at the MIT Keller Gallery through June 30, examines 20th- and 21st-century efforts to transform computing into a medium for creative production and aesthetic judgment in architecture and the applied arts. Drawing on philosophy, mathematics, computer science, and design computation, the exhibition

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LLMs help robots understand vague instructions and focus on key details

Aeronautical and astronautical engineering, ai, AI (Artificial Intelligence), Algorithms, Artificial Intelligence, automation, Computer Science and Artificial Intelligence Laboratory (CSAIL), Computer science and technology, Electrical engineering and computer science (EECS), Machine Learning, MIT Schwarzman College of Computing, Natural Language Processing, Research, Robotics, School of Engineering

Imagine working at a warehouse or office sometime in the near future, and you’re asked to help a new trainee learn the basics of their job. The catch: It’s a robot. To teach them, you might want to play a game of “show and tell” — that is, physically showing how to do something a

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Improving the speed and energy-efficiency of AI agents

ai, AI (Artificial Intelligence), Algorithms, Artificial Intelligence, Computer Science and Artificial Intelligence Laboratory (CSAIL), Computer science and technology, data, Defense Advanced Research Projects Agency (DARPA), Electrical engineering and computer science (EECS), Electronics, Machine Learning, MIT Schwarzman College of Computing, Research, School of Engineering, Software, Sustainability

Agentic workflows are artificial intelligence-powered software systems that chain together multiple models and external tools to tackle complicated tasks, like analyzing a video and answering questions about it.But the way these highly fragmented systems are designed and deployed often causes inefficiencies that can lead to wasted computation, energy, and cost. To improve efficiency, researchers from MIT

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Exploring the societal impacts of AI

Aeronautical and astronautical engineering, ai, AI (Artificial Intelligence), Algorithms, Artificial Intelligence, Computer Science and Artificial Intelligence Laboratory (CSAIL), Computer science and technology, Democracy, Economics, Electrical engineering and computer science (EECS), government, Labor and jobs, MIT Schwarzman College of Computing, MIT Sloan School of Management, Political science, Politics, Program in STS, Robotics, School of Engineering, School of Humanities Arts and Social Sciences, Special events and guest speakers, Technology and society

At the recent AI and Society Forum at MIT, experts from across the Institute discussed the potential benefits and dangers of technological innovation on labor, the nature of work, civil discourse, election administration, and other topics.The event featured individual research presentations and panel discussions, as well as a musical performance exploring the use of generative artificial intelligence

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New chip could help tiny robots traverse complex environments

Aeronautical and astronautical engineering, ai, AI (Artificial Intelligence), Algorithms, Artificial Intelligence, Augmented and virtual reality, Autonomous vehicles, Computer chips, Computer science and technology, Electrical engineering and computer science (EECS), Energy, Machine Learning, Memory, MIT Schwarzman College of Computing, National Science Foundation (NSF), Research, Research Laboratory of Electronics, Robotics, School of Engineering

A new chip developed by MIT researchers could help tiny, low-power UAVs avoid obstacles as they zip around tight corners inside an industrial HVAC system to check for gas leaks.The chip allows small autonomous robots and other battery-limited devices to construct detailed 3D maps of their environments in real-time using only about as much power

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In game theory, generalists sometimes win out over specialists

ai, AI (Artificial Intelligence), Algorithms, Artificial Intelligence, Electrical engineering and computer science (EECS), Game theory, Human-computer interaction, Laboratory for Information and Decision Systems (LIDS), Machine Learning, MIT Schwarzman College of Computing, Research, School of Engineering, Software

Whether you’re playing poker against a single opponent or find yourself in a bidding war over a home purchase with another prospective buyer, you are operating under conditions of imperfect information. You know what cards you’re holding in the poker game, and you also know how much above the home’s asking price you can afford,

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Could AI tell you where you left your keys?

Aeronautical and astronautical engineering, ai, AI (Artificial Intelligence), Algorithms, Artificial Intelligence, Autonomous vehicles, Computer science and technology, Computer vision, Electrical engineering and computer science (EECS), Laboratory for Information and Decision Systems (LIDS), Machine Learning, MIT Schwarzman College of Computing, Research, Robotics, School of Engineering

An auto factory worker can remember the storage bin where she left a partly assembled component the night before, and quickly return to that spot to pick it up. But robots that may work side-by-side with her would struggle to develop and access this same type of “spatiotemporal” memory.Now, MIT researchers have developed a long-term

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When it comes to predicting people’s preferences, it pays to consider “the power of three”

ai, AI (Artificial Intelligence), Algorithms, Artificial Intelligence, Computer modeling, Computer Science and Artificial Intelligence Laboratory (CSAIL), Computer science and technology, Electrical engineering and computer science (EECS), Human-computer interaction, Laboratory for Information and Decision Systems (LIDS), Machine Learning, Mathematics, MIT Schwarzman College of Computing, Research, School of Engineering

In his 1927 paper, “A law of comparative judgment,” the American psychologist L. L. Thurstone proposed that when people select one option among multiple alternatives, they are picking the one that has the highest value to them, even though they cannot assign a particular number to that choice. Thurstone was a pioneer of “psychometrics” — a

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NSF renews support for MIT-led AI and physics institute, expanding a new model for discovery

ai, AI (Artificial Intelligence), Algorithms, Artificial Intelligence, Astronomy and astrophysics, Chemistry, Collaboration, Computer science and technology, data, DMSE, Funding, IDSS, Machine Learning, Mathematics, MIT Schwarzman College of Computing, National Science Foundation (NSF), Physics, Research, School of Engineering, School of Science

The MIT-led Institute for Artificial Intelligence and Fundamental Interactions (IAIFI) has received renewed support from the National Science Foundation (NSF) for an additional five years, increasing annual funding from $4 million to $4.98 million. The renewal marks a new phase for IAIFI, which has spent its first five years building a research model and an

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