30 citations · 48 across the 5 of their papers we have counts for
5 papers
Magentic-UI: Towards Human-in-the-loop Agentic Systems
Hussein Mozannar, Gagan Bansal, Cheng Tan +17
AI agents powered by large language models are increasingly capable of autonomously completing complex, multi-step tasks using external tools. Yet, they still fall short of human-l…
Optimizing Sequential Multi-Step Tasks with Parallel LLM Agents
Enhao Zhang, Erkang Zhu, Gagan Bansal +3
Large language model (LLM)-based multi-agent systems have demonstrated remarkable promise for tackling complex tasks by breaking them down into subtasks that are iteratively planne…
Interactive Debugging and Steering of Multi-Agent AI Systems
Will Epperson, Gagan Bansal, Victor Dibia +4
Fully autonomous teams of LLM-powered AI agents are emerging that collaborate to perform complex tasks for users. What challenges do developers face when trying to build and debug…
Magentic-One: A Generalist Multi-Agent System for Solving Complex Tasks
Adam Fourney, Gagan Bansal, Hussein Mozannar +17
Modern AI agents, driven by advances in large foundation models, promise to enhance our productivity and transform our lives by augmenting our knowledge and capabilities. To achiev…
Resonance: Replacing Software Constants with Context-Aware Models in Real-time Communication
Jayant Gupchup, Ashkan Aazami, Yaran Fan +23
Large software systems tune hundreds of 'constants' to optimize their runtime performance. These values are commonly derived through intuition, lab tests, or A/B tests. A 'one-size…