I'm looking into automating some of our reporting tasks. I want an agent that can write Python code, execute it against a SQL database, and then generate a summary. I've seen the mentioned for "Sandbox Agents." Does this mean the SDK provides a safe environment for code execution, or do I need to set up my own Docker containers for this?
3 answers
The newest versions of the OpenAI Agents SDK introduce a "Sandbox" capability that is quite powerful for data science. It provides a standardized infrastructure for agents to work across files and tools. While you can use a local sandbox for development, for production, it’s designed to work with hosted sandboxes. It basically creates a controlled environment where the agent can install dependencies, run code, and inspect files. This is perfect for your SQL-to-summary workflow, as the agent can write the query, run it, and then process the resulting CSV file all within the same session.
The integration with Pydantic for the data schemas makes the whole pipeline very robust and less prone to "hallucinated" data types.
Agree. Having that type safety in a data-heavy workflow is non-negotiable for us.
The SDK includes snapshotting and rehydration features. If a container fails or a session times out, the SDK can restore the agent's state from the last checkpoint. This makes it much more reliable for "long-horizon" tasks that might take a few minutes to complete. It essentially saves the filesystem state and the conversation history so the agent can pick up right where it left off, which is a massive upgrade over basic stateless API calls we were using previously.