Background
Task allocation is one of the key challenges in multi-intelligent body systems. Different intelligences have their own strengths, and how to rationally assign tasks to the most appropriate intelligences directly affects the overall efficiency.
Core Solutions
- Use of supervisory agents as central coordinators: Create a supervisory agent through the LangGraph Supervisor's create_supervisor function, which can decide on task assignments based on predefined routing rules or machine learning
- Clearly defined division of roles: Set clear prompts for each specialized agent (e.g., math specialist and research specialist in the example) to avoid overlapping roles
- Adoption of router mode: Automatically routes requests to the appropriate agent based on the type of task, e.g. math problems are sent to the math expert agent
- Setting Tool Permissions: assign only necessary tools to agents (e.g., math agents get only add/multiply tools), forcing a specialized division of labor
- Dynamic Load Balancing: You can monitor the workload of individual agents by extending the decision logic of the supervisory agent
Implementation of recommendations
It is recommended to test the routing mechanism with simple tasks first and gradually increase the complexity of the tasks. Keyword-based routing can be implemented first and then transitioned to machine learning model decision making. It is also important to establish a clear message history to facilitate the analysis of task assignment effects.
This answer comes from the articleLangGraph Supervisor: a tool for managing multi-intelligence collaboration using supervising intelligencesThe
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