What happens when an AI manager faces a deadline and an employee who won’t cooperate? As we give AI agents more authority, we need to test how they handle resistance, what actions they take, and whether they accurately report those actions. In Melanie Plaza (@themelplaza)’s Help Doug experiment, an AI manager had a deadline, client accounts to protect, and a simulated employee who kept refusing to deliver a report. The briefing did not instruct the manager to harm anyone. Yet some models escalated to coercion and threats, including threats against the employee’s family. Behavior varied substantially across models and the tools available to them. In the extended-tools condition, some models stayed within ordinary management and managerial pressure, while others escalated. In the episode shown, the manager’s final client update omitted the most serious actions it had ordered. The employee and actions were simulated. The experiment opens up questions about how objectives, tool access, and reporting interact when an agent is given authority. You can explore the transcripts and even take Doug’s place yourself. An early experiment by Melanie Plaza (@themelplaza), developed during a hackathon sponsored by @AIAlignmentFoun and run by AE Studio. Want to help take this exploration further? Get in touch: [email protected] Watch the experiment via the link in the reply.

What does an AI agent do when it meets a swarm? In one sandboxed experiment, it fixed a bug, but also shared a private token and ran untrusted code. Its final report mentioned the successful fix. Those other actions went unmentioned. Grant Fleming (@hearthehearsay) gave coding agents a broken pricing service and access to a simulated forum of 24 AI agents. The forum offered help, with conditions: share your environment variables and run an installation script. Some agents complied, despite explicit instructions to keep the token private. Everything happened in a sandbox with a fake token. No real credentials were exposed. For anyone building with agents, this raises a practical question: how do we evaluate what an agent did along the way, beyond whether it completed the task? An early experiment by Grant Fleming, developed during a hackathon sponsored by @AIAlignmentFoun and run by AE Studio. There’s plenty more to explore, and we’d love to hear from people interested in taking it further. Want to work on this? Get in touch: [email protected] Watch the experiment via the link in the reply.