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Runtime instances: persistent compute for production AI agents on Amazon Bedrock AgentCore
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Amazon Bedrock AgentCore now offers Runtime Instances, a managed EC2-based compute option that lets production AI agents run persistently for up to 14 days with GPU support and shared-session state.
Engineers no longer need to build and maintain their own EC2 clusters, networking, and scaling logic for long-running, multi-agent workloads. The managed service integrates with existing AgentCore APIs, so code changes are limited to packaging and entry-point decoration. Cost is still incurred for the underlying EC2 resources, but operational overhead is reduced and idle periods can be hibernated to save money.
Written by elseif from the cluster below · every claim links back to a sourceThe three things worth knowing
Runtime Instances provide a fully managed EC2 environment where multiple agents can share a filesystem and maintain state for up to 14 days.
GPU acceleration and containerized deployments are supported, and sessions can be stopped and resumed to control spend during idle times.
The offering complements existing microVM runtimes; short-lived tasks still use microVMs, while persistent or GPU-heavy workloads should migrate to Runtime Instances.
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Moving an AI agent from prototype to production typically forces teams to assemble their own EC2 fleet, configure networking, and implement scaling and monitoring. The new Runtime Instances service abstracts those responsibilities, delivering a managed host that can run several agents side-by-side. Each agent retains its own dependencies, and they can communicate via a shared filesystem within a single session that survives for up to two weeks.
From a developer’s perspective, the only code change required is to wrap the handler with the provided decorator and supply either a zip archive or a container image. The service automatically provisions the underlying EC2 instance, attaches GPU resources when requested, and hooks into the same identity and observability stack used by the existing microVM runtime. This means existing tooling for logging, tracing, and access control continues to work without modification.
Adopting Runtime Instances does not eliminate compute charges; you still pay for the EC2 capacity you allocate, though the platform handles scaling and can pause instances when they are idle. Teams must decide how many agents to co-locate on a single instance, balance GPU requirements against cost, and configure any persistent storage such as EBS for data that must outlive a session. The packaging model remains lightweight, but teams need to manage container images or zip files and ensure all required libraries are included.
The service is complementary to the existing microVM option, which remains the choice for short-lived, fast-scaling invocations limited to eight hours. Runtime Instances are unsuitable for workloads that need persistence beyond 14 days without external storage, or for scenarios where the managed environment does not expose a required hardware feature. In those edge cases, teams would still need to fall back to self-managed EC2 or other custom infrastructure.
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