Iterative learning is achieved through error feedback loops:
- The system catches an exception when the execution agent calls the API and returns an error (e.g. HTTP 404/500)
- Observation agents analyze the cause of errors (e.g., missing parameters/wrong formatting) and generate suggestions for corrections
- Base agent adapts the original instruction to the recommendation (e.g., supplementing required fields)
- The execution agent re-initiates the call to form a new execution cycle
For example, if the REQUIRED parameter is missing when querying a movie, the system learns that the YEAR field needs to be supplemented by an error message from the TMDB. This mechanism enables the intelligent body to gradually master the specification of the use of the tool, and eventually realize adaptive learning with zero or few samples.
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 exactly does CoAgents' iterative learning mechanism work?
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