| name | tcandon-multi-agent-router |
| title | TCAndon-Router: Adaptive Reasoning Router for Multi-Agent Collaboration |
| version | 0.0.2 |
| engine | skillxiv-v0.0.2-claude-opus-4.6 |
| license | MIT |
| url | https://arxiv.org/abs/2601.04544 |
| keywords | ["multi-agent","routing","task-assignment","dynamic-adaptation","agent-collaboration"] |
| description | Route queries to multiple specialized agents dynamically using reasoning-aware routing that generates natural-language justification before predicting candidate agents. Enables seamless addition of new agents without system redesign. Routes aggregate responses from multiple specialists into coherent final answers, supporting enterprise-scale multi-agent systems with overlapping capabilities. |
Problem
Multi-agent systems face critical routing challenges:
- Static Routing Bottleneck: Traditional single-label routing (query → one agent) can't leverage multiple specialists with overlapping skills
- New Agent Integration: Adding agents requires retraining routers and redesigning routing logic
- Capability Overlap Conflicts: When multiple agents could handle a query, picking one wastes alternative perspectives
- Ambiguous Intent: Many queries don't cleanly map to a single agent; forcing 1:1 assignment loses information
Current routers treat query-to-agent mapping as a classification problem, but real-world queries often benefit from multiple specialized perspectives.
Solution
TCAndon-Router (TCAR) introduces Multi-Candidate Reasoning-Aware Routing:
- Reasoning-First Routing: Generate natural-language reasoning chain explaining why an agent is appropriate before assigning queries
- Multi-Candidate Assignment: Predict a set of candidate agents rather than single agent
- Lazy Agent Integration: New agents register themselves; router adapts without retraining
- Response Refinement: Aggregate responses from multiple agents with a dedicated Refining Agent producing coherent final answer
When to Use
- Enterprise Multi-Agent Systems: Routing queries to teams of specialized agents (customer service, technical support)
- Overlapping Expertise: Domains where multiple agents have relevant but complementary knowledge
- Scalable Agent Networks: Systems that grow from 5 to 100+ agents over time
- High-Confidence Requirements: Critical decisions benefiting from multiple agent perspectives
- Exploratory Agents: Research systems where diverse viewpoints improve answer quality
When NOT to Use
- For single-agent systems (router adds unnecessary overhead)
- In latency-critical applications (multi-agent routing adds response time)
- When computational budget for running multiple agents is unavailable
- For tasks with clear single-agent ownership (no overlap)
Core Concepts
The framework operates on the principle that reasoning improves routing:
- Interpretable Decisions: Before assigning agents, explain why in natural language
