The CoAgents framework solves this problem perfectly through the mechanism of multi-intelligence collaboration. It breaks down the process into three key steps.
- Basic AgentSpecializes in generating accurate instructions, translating user intent into an instruction format that the tool understands
- executing agentFocus on executing instructions accurately, calling the right tools and parameters
- Observation AgentsReturns are analyzed and validated to ensure that they meet expectations
When the result does not match the instruction, the system automatically triggers an iterative correction: the executing agent adjusts the invocation method, and the observing agent reextracts the information, forming a closed-loop feedback. This design greatly reduces the amount of manual work and improves the accuracy of tool usage.
This answer comes from the articleCoAgents: a framework for learning to use tools through multi-intelligence collaborationThe
May not be reproduced without permission:AI productivity tools " How to solve the problem of mismatch between instruction generation and execution results when using external tools for large language models?
English 
简体中文 
日本語 
Deutsch 
Português do Brasil