Multi-Intelligence Context Management Best Practices
A structured control strategy is required to resolve contextual clutter in multi-intelligence collaboration:
- Task Partitioning Design::
- Design clear task splitting logic for master intelligences (e.g. by function/step)
- Define clear input and output specifications for each sub-intelligence
- context-sensitive technology::
- Allocating separate memory subdirectories for different intelligences
- Using the /prompts directory to create dedicated system prompts
- monitoring mechanism::
- Real-time view of the execution branches of each intelligence in the web interface
- Checking Context Delivery Consistency with Log Comparison
- Recovery program::
- Available when confusion is detected:
- Terminate the current task with the "stop all agents" command.
- Restart the mission after clearing conflicting memory fragments
- Adjust temperature to reduce output randomness
- Available when confusion is detected:
- protective measure::
- Control the number of concurrent intelligences (no more than 5 recommended)
- Designing check node mechanisms for complex tasks
Through the above methods, a multi-intelligence body collaboration system with clear hierarchy and clear boundaries can be established, significantly reducing the risk of contextual confusion.
This answer comes from the articleAgent Zero: An Open Source AI Intelligent Body Framework for Flexible Creation and Execution of TasksThe
May not be reproduced without permission:AI productivity tools " How to Overcome Agent Zero's Contextual Confusion in Multi-Intelligence Collaboration?
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