Agentic workflows — AI-powered software systems that chain multiple models and tools to complete complex tasks — are becoming the backbone of cloud computing. But their fragmented design often wastes computation, energy, and money. Researchers from MIT and Microsoft have developed a new system called Murakkab that streamlines the design and deployment of these workflows, automatically optimizing them for speed, energy efficiency, and cost.
With Murakkab, developers describe their application’s goal in plain language, and the system automatically selects the best AI models, tools, hardware configurations, and resource allocations. It adjusts these on the fly based on user priorities, such as minimizing costs or maximizing speed. In tests, Murakkab used only about 35% of the computation, 27% of the energy, and under 25% of the cost compared to traditional methods — without sacrificing performance.
“Agentic workflows are getting very complicated and quickly becoming the backbone of what cloud providers are doing,” says Gohar Chaudhry, an MIT EECS graduate student and lead author of the paper presented at USENIX OSDI. “Energy usage is a huge concern, so we need to be very careful about how efficient these workflows are.”
Murakkab also adapts dynamically when new models or hardware become available, eliminating the need for developers to manually reconfigure their systems. The researchers plan to expand the system to more complex workflows and larger computing clusters.
