Collaboration Principles for Distributed Intelligent Agents
The core innovation of the Sage framework is its multi-agent collaborative system, in which 6 types of specialized agents have their own roles and work closely together. The analysis agent uses thought chain technology to understand the context of the task; the decomposition agent uses graph algorithms to establish sub-task dependencies; and the planning agent optimizes the execution path based on reinforcement learning. This division of labor allows complex tasks such as "generating a market analysis report" to be automatically broken down into subtasks such as data collection, trend analysis, chart generation, and so on, and processed in parallel.
The system realizes real-time communication between agents through FastAPI, and the work status is visualized in the web interface. In the test, multi-agent collaboration can shorten the completion time of complex tasks by 60% compared with single-agent solutions. the unique observation agent mechanism continuously monitors the quality of tasks, and automatically triggers the error recovery process when detecting anomalies, which ensures that the stability of the system meets the SLA standard of 99.9%.
This answer comes from the articleSage: An Intelligent Multi-Agent Task Decomposition and Collaboration FrameworkThe
English 
简体中文 
日本語 
Deutsch 
Português do Brasil