Runtime instances: persistent compute for production AI agents on Amazon Bedrock AgentCore should be explained through that lens before any broad claim is made. When you move AI agents from prototype to production, the infrastructure challenges multiply. Today, I’m happy to announce runtime instances, a new complementary compute option in Amazon Bedrock AgentCore Runtime that gives your agents persistent, managed infrastructure purpose-built for complex agent workloads. The piece keeps the context, impact, and follow-up signals in view so readers do not stop at the headline.
What happened
When you move AI agents from prototype to production, the infrastructure challenges multiply. Your agents need to persist state across multi-step workflows that run for hours or days. They need to coordinate with other agents, share context, and sometimes access GPUs for specialized tasks. Amazon Bedrock AgentCore runtime microVMs provide a fully managed environment for invocations that can run for up to 8 hours and support stateful workflows through managed session storage. Some workloads also benefit from dedicated, larger-capacity environments — for example, when agents need to run continuously for multiple days, access GPUs or the underlying OS, or run multiple collaborating agents on the same host.
Practical impact for readers
Today, I’m happy to announce runtime instances, a new complementary compute option in Amazon Bedrock AgentCore Runtime that gives your agents persistent, managed infrastructure purpose-built for complex agent workloads.
Details worth verifying
Before today, if you wanted to keep your agents running for days or they needed GPU access, or multi-agent coordination, you had to build and manage that infrastructure yourself. You provisioned EC2 instances, configured networking, set up session management, handled scaling, and stitched together monitoring. Runtime instances handles all of that for you while integrating with the same AgentCore APIs, identity controls, and observability you already use with AgentCore Runtime microVMs.
Who should act or wait
A few things that should make agent developers smile: your agents can call each other as tools within a shared session, iterating autonomously until the job is done. You bring any framework ( CrewAI , LangGraph , LlamaIndex , Strands) and any model. Packaging is minimal, a @app. entrypoint decorator and a zip file or container image. And if your workflow spans days, hibernate Monday night and resume Wednesday morning with everything intact.
What is still unclear
Runtime microVMs and runtime instances are complementary compute options that you can use independently or together through the same AgentCore runtime APIs. A lightweight orchestrator agent on runtime microVM can coordinate and dispatch work to specialized worker agents running on instances. The orchestrator handles API calls, task routing, and result aggregation using runtime microVM’s fast scaling, while workers on Instances perform compute-intensive tasks like code compilation, security scanning, or GUI automation that require persistent state and direct OS access.
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