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AgentGate is introduced as a structured routing engine for dispatching requests in an Internet of Agents, framing routing as a constrained decision problem with action decision and structural grounding stages. They fine-tune compact 3B-7B parameter models using a routing-oriented scheme with candidate-aware supervision and hard negative examples. Results on a curated benchmark demonstrate that compact models can achieve competitive routing performance, with model differences primarily affecting action prediction, candidate selection, and grounding quality.
Forget monolithic LLMs – AgentGate shows that smaller, fine-tuned models can efficiently route requests in multi-agent systems, opening the door to resource-constrained deployments.
The rapid development of AI agent systems is leading to an emerging Internet of Agents, where specialized agents operate across local devices, edge nodes, private services, and cloud platforms. Although recent efforts have improved agent naming, discovery, and interaction, efficient request dispatch remains an open systems problem under latency, privacy, and cost constraints. In this paper, we present AgentGate, a lightweight structured routing engine for candidate-aware agent dispatch. Instead of treating routing as unrestricted text generation, AgentGate formulates it as a constrained decision problem and decomposes it into two stages: action decision and structural grounding. The first stage determines whether a query should trigger single-agent invocation, multi-agent planning, direct response, or safe escalation, while the second stage instantiates the selected action into executable outputs such as target agents, structured arguments, or multi-step plans. To adapt compact models to this setting, we further develop a routing-oriented fine-tuning scheme with candidate-aware supervision and hard negative examples. Experiments on a curated routing benchmark with several 3B--7B open-weight models show that compact models can provide competitive routing performance in constrained settings, and that model differences are mainly reflected in action prediction, candidate selection, and structured grounding quality. These results indicate that structured routing is a feasible design point for efficient and privacy-aware agent systems, especially when routing decisions must be made under resource-constrained deployment conditions.
