Flow makes multi-agent systems smarter by letting them reorganize their tasks on the fly.
Flow introduces a modular approach to multi-agent systems that dynamically updates workflows during execution, enabling parallel task processing and efficient error handling.
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https://arxiv.org/abs/2501.07834
🤖 Original Problem:
Current multi-agent frameworks using LLMs lack flexibility in adjusting workflows during task execution, leading to inefficient sequential processing and poor handling of unexpected challenges.
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🔧 Solution in this Paper:
→ Flow represents workflows as Activity-on-Vertex (AOV) graphs, where each node is a subtask and edges show dependencies.
→ The system generates multiple candidate workflows and selects the one with highest parallelism and lowest dependency complexity.
→ During execution, Flow continuously monitors task progress and dynamically updates the workflow based on performance data.
→ When a subtask fails, the system can modify, add, or remove tasks while maintaining workflow coherence through modular design.
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💡 Key Insights:
→ Modularity in workflow design enables parallel execution and easier dynamic updates
→ Additional dependencies in workflows reduce expected success rates of subtasks
→ Dynamic workflow updates significantly improve error handling and task completion
→ Parallel execution of subtasks reduces overall processing time
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📊 Results:
→ 93% average success rate across tasks vs 66.7% for AutoGen and 71% for MetaGPT
→ 3.54/4 human satisfaction rating compared to 2.75 for AutoGen
→ 87-93% success rate in error handling with dynamic updates vs 0-67% without updates
→ 80% reduction in execution time compared to baseline approaches
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