activity
20232026
most citedAutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversation

178 citations · 200 across the 8 of their papers we have counts for

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5 papers · 1 filter

cs.AI2026

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…

cs.AI20253 cited

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…

cs.AI20251 cited

Measuring AI agent autonomy: Towards a scalable approach with code inspection

Peter Cihon, Merlin Stein, Gagan Bansal +2

AI agents are AI systems that can achieve complex goals autonomously. Assessing the level of agent autonomy is crucial for understanding both their potential benefits and risks. Cu…

cs.AI202415 cited

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…

cs.AI2023178 cited

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…