Organizations are building AI agents that autonomously write code, query databases, and interact with internal systems on behalf of their teams. These agents handle use cases such as automated code review, data pipeline optimization, and infrastructure troubleshooting. When your AI agent generates a shell command, queries a database, or writes to a file system, that code needs a secure environment to run in. Without isolation, one session’s tool calls can contaminate another session’s state, inadvertently expose sensitive data across tenants, or unintentionally allow untrusted code to reach production resources.
Self-hosted sandboxes solve this by keeping agent execution within your own AWS account, giving you full control over networking, secrets, and governance. Say you’re building an internal AI agent that optimizes database queries for your engineering team. A developer asks it to find the ten slowest queries in your analytics database, rewrite them with better indexing, and test the results. That’s three tool calls in a single session. One hits a live database with real credentials. Now multiply that by fifty developers using the assistant at the same time.
Each session needs its own credentials, its own filesystem, its own network boundary. If credentials or state cross session boundaries, you have inadvertent data exposure. AWS Lambda MicroVMs is a serverless compute environment that provides general-purpose runtimes with the strong isolation of virtual machines and the rapid scaling of AWS Lambda . Powered by Firecracker virtualization, each MicroVM runs Amazon Linux with full OS access for up to 8 hours. You launch, suspend, resume, and terminate MicroVMs programmatically.
You get the serverless benefits of managed infrastructure, responsive scaling, and pay-per-use pricing. Three capabilities make Lambda MicroVMs a strong fit for agent sandboxes: In this post, we show you how to architect and build a self-hosted AI agent that uses Lambda MicroVMs as secure, isolated sandboxes for tool-call execution. Lambda MicroVMs can handle the compute isolation for running tool calls, while the host for production AI agents, such as Amazon Bedrock AgentCore, manages the agent logic, model routing, and session state. A complete reference solution is available in aws-samples .