OpenAI Agents SDK adds native sandbox
OpenAI has updated the Agents SDK with a model-native harness and native sandbox execution, aimed at making agents more production-ready without forcing teams to assemble their own orchestration layer. The new setup is designed for long-horizon tasks where agents need to inspect files, run commands, and execute code safely across isolated environments, with support from providers like E2B, Modal, Daytona, and Vercel. It pushes the SDK closer to a full agent runtime rather than just a thin wrapper around model calls.
The big shift here is that OpenAI is trying to own more of the agent runtime, not just the model interface. That matters because the hard part of agents is increasingly execution safety and coordination, not prompt quality.
- –The native harness makes the SDK more opinionated, which should reduce glue code and production drift.
- –Built-in sandboxing is the right move for long-running, tool-heavy workflows where isolation matters.
- –Provider support is a practical signal: OpenAI seems to want portability instead of locking teams into one execution backend.
- –This is a stronger fit for real software agents than demo-only assistants, especially where files, commands, and code execution are involved.
- –The tradeoff is less architectural freedom for teams that already have custom orchestration stacks.
DISCOVERED
57d ago
2026-04-17
PUBLISHED
57d ago
2026-04-16
RELEVANCE
AUTHOR
[REDACTED]