A new research paper proposes an autonomous event-driven multi-agent orchestration system designed for enterprise AI at scale. The study evaluates two architectures, DAG Plan and Execute and ReAct, across various enterprise scenarios and scales. Results indicate that performance degradation at larger scales is primarily due to agent discovery noise, with simple tasks being more affected. A novel Task Manager was introduced to improve continuous operation by reducing latency and enhancing event correctness. AI
IMPACT This research could enable more robust and scalable AI systems for complex enterprise tasks.
RANK_REASON Research paper published on arXiv detailing a new approach to multi-agent systems. [lever_c_demoted from research: ic=1 ai=1.0]
- alphaXiv
- arXiv
- CatalyzeX
- DAG Plan and Execute
- DagsHub
- Enterprise
- Gotit.pub
- Hugging Face
- Persona
- ReAct
- ScienceCast
- Task Manager
AI-generated summary · Google Gemini · from 1 sources. How we write summaries →
