30 citations · 72 across the 13 of their papers we have counts for
13 papers
SentinelBench: A Benchmark for Long-Running Monitoring Agents
Matheus Kunzler Maldaner, Adam Fourney, Amanda Swearngin +5
AI agents are increasingly asked to carry out work that spans minutes, hours, or longer. Yet the default model of agent behavior is continuous action: issuing tool calls, refreshin…
Overseeing Agents Without Constant Oversight: Challenges and Opportunities
Madeleine Grunde-McLaughlin, Hussein Mozannar, Maya Murad +3
To enable human oversight, agentic AI systems often provide a trace of reasoning and action steps. Designing traces to have an informative, but not overwhelming, level of detail re…
The Collaboration Gap: Exploration and Benchmarking of Open-World Agentic Cooperation
Tim R. Davidson, Adam Fourney, Saleema Amershi +3
The trajectory of AI development suggests that we will increasingly rely on agent-based systems powered by language models, composed of independently developed agents with differen…
Magentic Marketplace: An Open-Source Environment for Studying Agentic Markets
Gagan Bansal, Wenyue Hua, Zezhou Huang +21
As LLM agents advance, they are increasingly mediating economic decisions, ranging from product discovery to transactions, on behalf of users. Such applications promise benefits bu…
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…
Navigating Rifts in Human-LLM Grounding: Study and Benchmark
Omar Shaikh, Hussein Mozannar, Gagan Bansal +2
Language models excel at following instructions but often struggle with the collaborative aspects of conversation that humans naturally employ. This limitation in grounding -- the…
Challenges in Human-Agent Communication
Gagan Bansal, Jennifer Wortman Vaughan, Saleema Amershi +5
Remarkable advancements in modern generative foundation models have enabled the development of sophisticated and highly capable autonomous agents that can observe their environment…
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…
AutoGen Studio: A No-Code Developer Tool for Building and Debugging Multi-Agent Systems
Victor Dibia, Jingya Chen, Gagan Bansal +5
Multi-agent systems, where multiple agents (generative AI models + tools) collaborate, are emerging as an effective pattern for solving long-running, complex tasks in numerous doma…
Towards better Human-Agent Alignment: Assessing Task Utility in LLM-Powered Applications
Negar Arabzadeh, Julia Kiseleva, Qingyun Wu +5
The rapid development in the field of Large Language Models (LLMs) has led to a surge in applications that facilitate collaboration among multiple agents to assist humans in their…
Generation Probabilities Are Not Enough: Uncertainty Highlighting in AI Code Completions
Helena Vasconcelos, Gagan Bansal, Adam Fourney +2
Large-scale generative models enabled the development of AI-powered code completion tools to assist programmers in writing code. However, much like other AI-powered tools, AI-power…