Reduce your agent’s costs by 75% with GKE Agent Sandbox
In today’s agentic era, modern cloud applications are evolving from a set of passive tools to fleets of autonomous digital workers that reason, plan, and take action across a wide range of tasks.
For platform engineering teams designing these environments, the simplest approach is often to deploy an agent on to an open-source framework like OpenClaw and Hermes running on a virtual machine (VM). But as those workloads move into production and scale to support additional users or use cases, teams quickly hit a critical challenge: AI agents tend to operate in bursts; for a while they actively process requests or execute code, followed by long periods of inactivity while awaiting user input or external triggers. If you rely on static compute allocations, idle agents are still consuming valuable CPU and memory.
The question becomes: how do you safely pack more agents onto a fixed compute footprint without sacrificing reliability,...
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