Teamo uses a unique dual-protocol mechanism of A2A and MCP to guarantee the reliability of results through three core strategies:
- division of labor and collaboration: Different intelligences are responsible for different parts of the task (e.g., data acquisition, validation, integration) to avoid single model limitations
- cross-validation: Intelligentsia check each other's data sources and logical derivations, automatically flagging contradictory information and requesting revalidation
- competition mechanism: Multiple versions of the key findings are generated and the optimal solution is selected through an internal scoring system.
For example, in a market analysis task, one intelligent body might crawl data from the web, another would check the data timeliness, and a third would be responsible for comparing industry reports. This architecture reduces the error rate by more than 60% compared to a single-intelligent-body system.
This answer comes from the articleTeamo: a multi-intelligence collaboration platform for knowledge workThe
May not be reproduced without permission:AI productivity tools " How does the Teamo platform ensure accuracy of task results through multi-intelligence collaboration?
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