Jun 26, 2026
Improving the speed and energy-efficiency of AI agents
Imagine a busy AI system working behind the scenes — handling complex tasks like analyzing videos or generating code. Now, think about how inefficiently those workflows often run, wasting energy and money. That’s what MIT researchers, led by Gohar Chaudhry and Adam Belay, are tackling with a new system called Murakkab. ((slower)) It lets developers describe what they want in plain language, and then automatically figures out the best models, tools, and hardware configurations — on the fly. So, instead of painstakingly setting up every detail, the system dynamically adjusts to prioritize speed, cost, or energy savings, according to what’s needed. When tested, Murakkab cut energy use by over 70% and costs by nearly 75%, all while keeping performance high. As Chaudhry points out, in a landscape where agentic workflows are becoming the backbone of cloud services, making them resource-efficient isn’t just smart — it’s essential. That shift might seem subtle now, but it’s exactly the kind of innovation that shapes the next era of AI deployment.