Build safety and oversight into the loop
Use least privilege, explicit approval, and untrusted-data boundaries.
- Place safeguards around data access, tool actions, and high-impact outcomes.
An agent loop may access private data or change external systems. Give it the minimum permissions needed, keep authorization checks in software, and require explicit consent for tool actions as appropriate to the integration. Treat model output and tool results as untrusted data. For consequential decisions, keep people informed and provide a review or override path.
A small example
1action = {"name": "send_message", "approved": False}
2if action["approved"]:
3 print("Execute authorized action")
4else:
5 print("Pause for user approval")Pause for user approval
Use limits on what the loop can read, write, and send. Separate planning from execution and check every proposed action against policy. Make it possible to stop the loop, revoke access, and inspect what it did. Human review is especially important when errors could affect safety, money, rights, or privacy.
Key takeaways
Place safeguards around data access, tool actions, and high-impact outcomes.
Bound the loop, validate actions, and make its outcome observable.
Lesson quiz
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