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. 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.
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 strict workspace isolation, instant session resumption, elastic pay-per-use economics, and fluid multi-agent collaboration.
To meet these emerging demands, we’re expanding our AI storage portfolio and announcing availability of Filestore agent volumes, a new, fully managed capability purpose-built to deliver high-performance, elastic file storage for scaling agentic workloads on Google Cloud.
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 infrastructure or tenant data at risk. 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.
Filestore agent volumes within Google Cloud Filestore, give you purpose-built agentic storage to complement your agentic compute via a dynamic provisioning architecture designed specifically for the scale and elasticity of AI agent fleets. Co-designed with Agent Substrate to support agentic fleets at scale, Filestore agent volumes provide GKE sandboxes with instantaneous access to isolated, persistent file storage. When configured to leverage Filestore, GKE storage management happens behind the scenes: Every time GKE launches a sandbox for a new agent task, Filestore automatically allocates and attaches a dedicated, isolated file workspace to that environment in milliseconds. Platform teams don’t need to manually create, attach, or tear down storage volumes for individual agent runs; instead, the system handles the entire volume lifecycle automatically as your agent fleet scales up and down. The result is an efficient, end-to-end infrastructure solution for cost-effective agent management that provides:
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Granular isolation and enterprise guardrails: Agent platforms face security and data leakage risks when running untrusted, autonomous code. Filestore agent volumes enforce strict boundary controls and granular access permissions per workspace, ensuring agents operate exclusively within their designated directories and keeping dynamic toolchains strictly isolated across tenants.
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Sub-second session resumption: Traditional storage provisioning approaches can introduce cold-start latency that stalls interactive agent sessions. Agent volumes attach and detach in milliseconds, making it possible for orchestrators to aggressively suspend idle sandboxes to save compute costs, and resume instantly when new tasks or user inputs arrive.
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Smart lifecycle economics and pay-per-use pricing: Pre-allocating fixed-size, high-performance storage for thousands of short-lived or intermittent agent tasks can create massive storage waste. With agent volumes, platforms pay only for the storage capacity consumed and benefit from automatic lifecycle tiering. This means you get high performance without wasted spend: When your agents aren’t actively reading/modifying code or analyzing datasets, you can automatically shift idle workspace state to lower-cost storage.
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Multi-agent collaboration: Coordinating multi-agent swarms can result in brittle data-passing pipelines and risk of file collisions. Built with native Read-Write-Many (RWX) support and POSIX file locking, agent volumes allow orchestrators to attach a single shared workspace across multiple agents. Collaborating agents can safely co-author, test, and review project files concurrently with file-level consistency and protection against write conflicts.
Powering next-generation agentic workloads
By providing an elastic, high-performance, and isolated file tier, Filestore agent volumes unlock a wide spectrum of agentic workloads and use cases in production:
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Software engineering and coding sandboxes: Agentic coding platforms can spin up thousands of isolated workspaces where agents safely install libraries, write multi-file patches, run build tools, and execute unit tests, all leveraging standard POSIX file semantics with no need for storage-specific customization.
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Collaborative multi-agent swarms: Complex workflows, such as a lead orchestrator delegating tasks to dedicated research, code generation, and validation sub-agents, can directly share a unified file tree. RWX support allows agents to co-author and review project files concurrently without write conflicts.
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Interactive long-horizon workflows: For user-in-the-loop applications (such as agents that require asynchronous user approval or run multi-hour data analysis pipelines), platforms can suspend idle agent sandboxes to minimize compute waste, then resume execution on demand with sub-second responsiveness.
Get started today
If you are building an Agent-as-a-Service platform, scaling coding assistants, or deploying enterprise agent fleets, your storage tier should accelerate your innovation — not hinder it.
Filestore agent volumes are now available to all Google Cloud customers for non-production workloads. GA support for production workloads is available via allowlist. This new offering features out-of-the-box integrations with Agent Substrate on GKE and GKE Agent Sandbox to help you build responsive, scalable, and cost-efficient agent platforms today.
To request access to Filestore agent volumes, submit this form and visit the Filestore documentation and GKE documentation to learn more.
