Moltbot (formerly Clawdbot)'s multi-agent routing system is designed to efficiently distribute tasks among specialized AI agents, ensuring optimal performance and scalability. The system works by breaking down complex user requests into smaller, manageable subtasks, then routing these subtasks to the most suitable agent based on its expertise, availability, and current workload.
How It Works:
- Task Decomposition: When a user sends a request, the system analyzes it and splits it into logical components (e.g., data retrieval, analysis, response generation).
- Agent Specialization: Different agents are trained for specific functions—some handle natural language understanding, others manage data processing or API interactions.
- Dynamic Routing: A central controller evaluates agent capabilities, current queue status, and historical performance to assign subtasks dynamically. This ensures faster processing and reduces bottlenecks.
- Collaboration & Aggregation: Agents work in parallel, and their outputs are combined into a cohesive final response. The system may also include feedback loops to refine future routing decisions.
Example:
If a user asks, "Compare the latest iPhone and Samsung Galaxy in terms of camera, battery, and price," the system might:
- Route the camera comparison to an agent specializing in tech specs.
- Assign the battery analysis to another agent with access to power consumption data.
- Let a third agent fetch real-time pricing from e-commerce APIs.
The results are then merged into a single, well-structured answer.
This approach improves efficiency, scales with demand, and allows continuous optimization of agent workflows.
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