arXiv:2605.14241v2 Announce Type: replace Abstract: Tool-augmented LLM agents increasingly access the same tool type through multiple functionally equivalent providers, such as web-search APIs, retrievers, or LLM backends exposed behind a shared interface. This creates a provider-routing problem under runtime load: the router must choose among providers that differ in latency, reliability, and answer quality, often without gold labels at deployment time. We introduce LQM-ContextRoute, a contextual bandit router for same-function tool providers. Its key design is latency-quality matching: inste
The proliferation of functionally equivalent yet variable-performing AI tools necessitates sophisticated routing solutions to optimize LLM agent performance in real-time.
Efficient routing among AI tools directly impacts the cost, latency, and quality of AI agent operations, a critical factor for enterprise adoption and scaling.
The development of contextual bandit routers allows for dynamic optimization of tool selection based on real-time performance metrics, enabling AI agents to better leverage diverse underlying services.
- · AI Agent developers
- · Cloud providers offering diverse AI services
- · Enterprises deploying AI agents
- · Inefficient single-provider AI tool strategies
- · AI services with consistently poor latency or quality
AI agents become more efficient and reliable by intelligently selecting the best available tools dynamically.
This efficiency drives broader adoption and more complex applications of autonomous AI agents across industries.
Increased reliance on sophisticated routing mechanisms could create new vulnerabilities or points of control within the AI supply chain.
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Read at arXiv cs.LG