SAGE: Lookahead Action Gating for Safe, Budget-Aware Edge–Cloud AI Agents

Autonomous AI agents execute requests through multi-step trajectories in which each action can be generated by a fast local edge-based small language model (SLM) or a stronger cloud large language model (LLM). Existing hybrid approaches primarily select models to ensure the same output quality to stronger LLM in each step or preserve trajectories from LLMs, without jointly considering the recoverability of an SLM's action in the future trajectory and its long-term impact on end-to-end latency and cloud cost. An imperfect SLM action may remain recoverable by future LLM actions, while a seemingly faster SLM step may induce additional steps and ultimately incur higher end-to-end latency than escalating to the LLM immediately. Moreover, using LLM now may leave insufficient budget for more important future escalations. To address the problems, we present a lookahead Safe and budget-aware Action Gating system for Edge-cloud agents (SAGE), that accounts for these long-term effects on the future trajectory under end-to-end latency and cloud-cost budgets. First, it estimates whether accepting SLM preserves the agent's ability to successfully complete the request. Second, it jointly considers this safety estimate, the remaining budgets, and anticipated future resource needs to decide whether to execute the local action or escalate to the cloud LLM. Across two SLM/LLM pairs and six benchmarks, SAGE preserves 96.87–98.06% of LLM-Only accuracy while reducing normalized cloud cost to 0.41–0.45, and reducing mean latency by 25.9–28.6%. Compared with a state-of-the-art method, SAGE improves accuracy by 3.21–3.26 percentage points, reduces mean latency by 35.6%, and reduces normalized cloud cost by 37.5–40.6%.