Technical realization of collaborative systems
AutoAgent has a built-in industrialized multi-intelligent body collaboration engine and adopts the advanced architecture of task decomposition-assignment-aggregation. When a user submits a complex task, the system will automatically identify the type of task, split it into sub-tasks of different areas of expertise (e.g., search, analysis, visualization, etc.), and assign them to intelligences that specialize in different areas for collaborative completion. Each subintelligence is equipped with a specific tool chain and knowledge base.
Performance data
- In the GAIA benchmark test, Multi-Intelligent Body mode achieved a task completion accuracy of 92.3%
- 5-8 times more efficient than single intelligences in handling complex research problems
- Workflow orchestration that can manage dozens of intelligences in parallel
Typical Application Scenarios
For example, when a user requests to "analyze AI trends in 2025 and generate a visualization report", the system will be automatically deployed: searching intelligences to obtain the latest industry white papers, analyzing intelligences to extract key indicators, visualizing intelligences to create infographics, and finally coordinating intelligences to integrate and output the complete results. The whole process is fully automated without human intervention.
This answer comes from the articleAutoAgent: a framework for rapid creation and deployment of AI intelligences through natural languageThe
