E2B positions itself as the infrastructure layer beneath AI agents rather than an agent builder. The core idea is simple: each agent receives its own isolated machine, so code execution, browsing, and tool use happen in a sealed environment separated from your production systems.
The platform provides sandboxes that can be created from snapshots, paused, resumed, and forked, which makes parallel experimentation and reproducible runs practical. Agents can execute code, run tests and builds, and deploy to a sandbox URL. Deployment options include E2B’s own cloud or your AWS, GCP, or Azure account, with private subnets and egress controls.
It is aimed at developers and platform teams shipping agentic products, especially those who need isolation guarantees or must keep workloads inside their own cloud perimeter. Teams evaluating it should be comfortable with SDK-driven workflows and infrastructure concepts such as networking and snapshots.
A sensible approach is to start with a small sandbox prototype to validate latency and snapshot behavior, then move to self-hosted deployment if compliance requires it. E2B is less about building agents and more about giving them somewhere safe to run.
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