AI agents have moved past one-off prompts. They now run multi-step workloads for hours at a time. The length of task that frontier agents can complete on their own doubles about every four months (METR), and 40 percent of large enterprises are scaling AI agents, up from 27 percent a year ago (McKinsey). As agents run longer and with more autonomy, the focus shifts from what agents can do to how securely, reliably, and economically they can operate at scale. Agents do more than traditional software: they execute code, navigate browsers, access sensitive data, and act on behalf of users. These capabilities introduce new security and infrastructure demands, making strong isolation essential for production deployments.

Every agent session needs a secure, isolated environment. But delivering that isolation at scale introduces a fundamental challenge: balancing performance, cost, and efficiency. Pre-provisioned environments consume resources even when idle, driving up costs. On-demand environments reduce wasted capacity but introduce cold-start latency, delaying agent execution. Neither approach scales efficiently without trade-offs. As organizations move from deploying a handful of agents to running thousands of concurrent sessions, building and managing custom sandboxes becomes increasingly complex, expensive, and unpredictable.

The challenge is evident. Only 15 percent of organizations have scaled orchestrated, multi-agent systems, and 70 percent say they can’t yet trust and govern their agents ( Deloitte ). Scaling agents shouldn’t mean choosing between security, speed, and cost. The next generation of agent infrastructure must deliver strong isolation with fast startup times, elastic scaling, and usage-based economics.

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