Intent Hub: A Self-Healing Semantic Agent Routing System for Resolving Overlap in Agentic Systems

Intent Hub: A Self-Healing Semantic Agent Routing System for Resolving Overlap in Agentic Systems

Chenrui Liang, Peng Xu, Xinyuan Liu

Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence
Demo Track. Pages 8445-8448. https://doi.org/10.24963/ijcai.2026/976

Semantic overlap challenges accurate agent routing in large-scale agentic systems. We present Intent Hub, a self-healing semantic agent routing system that combines offline asynchronous HITL repair with online Dual Filtering. LLM-generated positive and adversarial negative utterances help construct explicit decision boundaries, enabling interpretable millisecond-level routing. Intent Hub further supports interactive semantic debugging, allowing developers to diagnose conflicts, repair rules, and observe online routing changes.
Keywords:
AI: Agent-based and Multi-agent Systems
AI: Humans and AI
AI: Natural Language Processing