OpenAI Agents SDK adds sandbox agents

This is OpenAI betting that agent frameworks need to look less like prompt glue and more like real application infrastructure. The interesting part isn’t just “multi-agent workflows” anymore; it’s the combination of tracing, human-in-the-loop controls, and sandbox execution that makes the SDK feel aimed at serious deployment, not demos.

• –Provider-agnostic support broadens the SDK beyond OpenAI-only stacks, which makes adoption easier for teams already standardized on mixed model providers.
• –Sandboxed agents and long-horizon execution are the real differentiator: they address the practical failure modes that show up once agents leave notebooks and hit actual work.
• –Built-in tracing and sessions reduce the amount of orchestration code teams have to write themselves, which is where many homegrown agent stacks become brittle.
• –The open-source nature matters here: OpenAI is trying to win the orchestration layer, not just the model layer, and that is a stronger lock-in surface.
• –Compared with lighter agent wrappers, this is closer to an opinionated runtime for production workflows than a convenience library.