From running build tools, to data analysis pipelines, to collaborative research, executing data-driven tasks is essential for any enterprise agent. Today, platform teams often stitch together custom workarounds to address agent storage requirements, which could include shuttling state back and forth between agent sandboxes and centralized storage or manually managing local disks and/or self-hosted file systems.
From running build tools, to data analysis pipelines, to collaborative research, executing data-driven tasks is essential for any enterprise agent.
Purpose-built storage for AI agent workspaces Autonomous agents require isolated runtime environments to safely execute dynamic code, install third-party packages, and run tools without putting host...
As organizations scale agent sandboxes to thousands or even millions of concurrent sessions, storage must evolve to overcome these trade-offs and meet the needs of these dynamic workloads, which require...
The page is ready to read now. The fuller skim-friendly version will appear here automatically.
From running build tools, to data analysis pipelines, to collaborative research, executing data-driven tasks is essential for any enterprise agent. However, as agent fleets scale, these approaches force difficult trade-offs between cold-start latency, operational complexity, and the cost of idle, pre-allocated storage. While Agent Substrate on GKE and GKE Agent Sandbox provide the dedicated compute environments needed to run high-density agent fleets, those sandboxes also need dedicated persistent workspaces to operate on.
Open the app view to save this story, compare related coverage, and continue from the same source.