TenonAgent: Virtualizing Execution Residency for Memory-Oversubscribed LLM Agents

Abstract

LLM agents execute as long-lived, multi-stage workflows with substantial resource-demand variation across phases. Provisioning each agent or sandbox for its lifetime peak leads to low resource utilization, particularly for memory, whereas aggressive oversubscription risks memory contention, out-of-memory failures, and costly workflow retries. This tension limits the concurrency and efficiency of agent serving under constrained host memory. We present TenonAgent, a runtime system that virtualizes sandbox residency by decoupling the logical agent's lifetime from its physical execution environment. To manage memory oversubscription, TenonAgent employs a dual-timescale control plane. At action boundaries, it combines predictive admission control with a fail-closed, restart-safe contract, dynamically materializing or dematerializing sandboxes based on global memory pressure. During active tool execution, a sched_ext-based kernel gate paces sandbox execution permissions to reduce unexpected concurrent memory growth. The gate provides feedback control over future execution opportunities. Evaluations across diverse agentic workloads show that, under the tested workloads and parameter settings, TenonAgent substantially reduces observed OOM failures and achieves a 100% task success rate in bursts reaching an 8.6x memory oversubscription ratio. Overall, it delivers up to a 5.5x speedup over unmanaged execution, outperforms existing agent-level and cgroup-based schedulers by 5x to 37x, and provides consistent benefits across both Docker and Firecracker microVM backends.