Multi-intelligence co-optimization scheme for AutoAgent
The system realizes efficient task decomposition through a three-tier architecture.
1. Dynamic task resolution layer
- After entering a complex task (e.g. "Market Research Report"), the system will.
- Automatic identification of verb nodes (collection/analysis/visualization)
- Creating a task topology through dependency syntactic analysis
- Estimates sub-task time and assigns weights intelligently
2. Intelligent body scheduling layer
- Preset Professional Intelligentsia Type.
- Scraper Agent
- Data Analyst (Analytics Agent)
- Report Generator (Reporter Agent)
- Support for exchanging structured data between intelligences via shared memory
3. Quality control layer
- Real-time checking of task progress
- Automatic retry of failed subtasks
- Perform consistency checks when aggregating final results
Optimization Recommendations.
- exist.envset up inTASK_TIMEOUT=300Adjusting the timeout threshold
- utilization@agent_nameAssigning specific intelligences to perform key steps
This answer comes from the articleAutoAgent: a framework for rapid creation and deployment of AI intelligences through natural languageThe
