The updated runtime executes every agent session inside an isolated Firecracker microVM with a dedicated identity, policy, and audit trail. Sessions start in under two seconds regardless of container size and can scale up to 8 vCPUs and 32 GB of memory. Early adopter Coinbase saw cold starts fall from nearly a minute to under five seconds during peak traffic while doubling agent volume. These technical upgrades allow enterprises to balance rigorous isolation boundaries against the operational costs of scaling autonomous software.
Key points
Enterprise adoption of multi-agent architectures has been hindered by complex governance…: Enterprise adoption of multi-agent architectures has been hindered by complex governance issues and costly idle infrastructure.
Faster session launches eliminate the historic need to maintain expensive pre-provisioned…: Faster session launches eliminate the historic need to maintain expensive pre-provisioned clusters or warm server pools.
Coinbase demonstrated that the environment reduces cold-start latency to under five secon…: Coinbase demonstrated that the environment reduces cold-start latency to under five seconds even amid doubling operational traffic.
Sources checked by our analysis pipeline: 1
Artificial Intelligence
Run agents securely and efficiently with the next generation AgentCore runtime
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…
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