178 citations · 226 across the 6 of their papers we have counts for
6 papers
Context as an Environment: Programmatic Context Management for Long-Horizon Agents
Yin Lin, Elaine Ang, Erkang Zhu +2
LLM agents increasingly take on long-running tasks whose history grows far beyond a single model context window. Existing approaches compress earlier interactions or extract select…
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…
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…
AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversation
Qingyun Wu, Gagan Bansal, Jieyu Zhang +11
AutoGen is an open-source framework that allows developers to build LLM applications via multiple agents that can converse with each other to accomplish tasks. AutoGen agents are c…