Control-Data Flow Separation Stabilizes Multi-Agent LLM Prompts
Key takeaways
- Prompt optimization in multi-agent LLMs is challenging due to entangled control and data.
- Control-data flow separation distinguishes structured protocols from unstructured content.
- This separation prevents prompt edits from corrupting execution-critical logic.
- The approach leads to stable prompt optimization and improved task performance.
Who benefits
Summary
This paper proposes "control-data flow separation" to stabilize prompt optimization in multi-agent LLM systems by distinguishing between structured, execution-critical control protocols and unstructured, task-relevant language. This design allows optimizers to improve task performance without risking pipeline failures due to accidental corruption of underlying execution logic.
Why it matters
Professionals building and managing complex multi-agent LLM systems can achieve more stable and reliable prompt optimization, reducing the risk of system failures while continuously improving task performance and operational efficiency.
How to implement this in your domain
- 1Analyze existing multi-agent LLM prompts for entangled control and data elements.
- 2Refactor prompts to explicitly separate execution protocols into structured objects.
- 3Implement validation mechanisms for control flow elements within agent pipelines.
- 4Develop prompt optimization strategies that target only the data flow components.
- 5Test the robustness and performance gains of separated control-data flow systems.
Original post by Wentao Zhang, Syed Shariyar Murtaza, Junaid Ahmad Bhatti, Utkarsh Soni, Yifan Nie, Eugene Wen, Yuntian Deng
"arXiv:2609.00621v1 Announce Type: new Abstract: Prompt optimization can improve multi-agent LLM systems, but the prompts being optimized often serve two entangled roles: generating task-relevant content and specifying execution-critical protocols, such as message routing, output…"
View on XOriginally posted by Wentao Zhang, Syed Shariyar Murtaza, Junaid Ahmad Bhatti, Utkarsh Soni, Yifan Nie, Eugene Wen, Yuntian Deng on X · view source
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