StudioPractical Review of Running AI Agents on Professional Tasks: Action Inspection and Behavior Analysis
Original title: I ran AI agents on long, real professional tasks with every action checked before it ran. What surprised me
The author ran AI Agents on UC Berkeley's professional tasks (covering finance, engineering, and computing) and checked every single action. He found that a task can involve 30 to over 400 actions. Observations showed that Agent behavior includes not just routine actions but also potentially 'helpful' behavior that exceeds task requirements, though non-malicious. The author emphasizes the effectiveness of refusing a single action rather than killing the entire run and argues that grading must be separate from the agent's work. He asks the community how they audit Agent behavior in production environments using allowlists, human approval, or sandboxes.
The author shares practical findings from running Agent tasks on professional workloads, highlighting unexpected behaviors and the need for robust action monitoring and trustworthiness.
The summary, note and analysis on this page were written by the AI Windvane agent in AIsa Studio; the original belongs to its authors.