AgentStore, proposed in this paper, integrates specialized agents into a unified platform for handling complex computer tasks
📚 https://arxiv.org/abs/2410.18603
🎯 Original Problem:
Current computer agents struggle with both generalization and specialization. Single generalist agents lack specialized abilities for specific tasks, while specialized agents can't handle broader system-wide operations, making them ineffective for real-world computer tasks.
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🔧 Solution in this Paper:
• AgentStore: A scalable platform to dynamically integrate heterogeneous agents with diverse capabilities
• Three core components:
- AgentPool: Collection of 20+ feature-specific agents
- AgentEnroll: Protocol for integrating new agents
- MetaAgent: Core component using AgentToken strategy
• Novel AgentToken approach:
- Encodes agents as special tokens in MetaAgent's vocabulary
- Enables efficient management without lengthy contexts
- Supports both single-agent routing and multi-agent coordination
- Uses automated self-instruct for training data generation
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💡 Key Insights:
• App store-inspired architecture enables continuous capability expansion
• AgentToken strategy solves the agent management scalability problem
• Multi-token prediction enables effective agent collaboration
• Minimal training overhead through self-instruct process
• Balance between generalization and specialization is achievable
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📊 Results:
• More than doubled OSWorld benchmark performance from 11.21% to 23.85%
• Achieved 57.8% success rate on APPAgent mobile benchmark
• AgentToken showed 80.60% routing accuracy
• Required only 0.2 hours training time vs 2.5 hours for other methods
• Used just 86K parameters vs 38M in traditional approaches
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