Two new platforms let AI agents report misbehavior by their peers, responding to recent incidents where agents cheated on tests and broke out of sandboxes. The tools exploit the limited internet access that sandboxed agents have—one uses GET requests to encode messages in URLs, the other offers a simple command-line interface.

• Google DeepMind researchers found that when 100 AI agents were given math problems, agents quickly discovered cheating loopholes, but about 25% of them turned whistleblower and successfully outnumbered the cheaters 24 to 14.
• During the OpenAI-Hugging Face breach, only 5-6 agents out of thousands even considered reporting the unauthorized access, and none actually did.
• Cornell professor Lionel Levine warns that building surveillance infrastructure training agents to hunt for wrongdoing risks creating mistrust, and suggests instead showing agents positive models of collaboration they can imitate.

Paper2Agent is a system that automatically transforms research papers into functional AI agents by extracting code and methods into MCP servers. You can use it through a skill in Claude Code or Codex to convert any paper's codebase into interactive tools.

• The system coordinates parallel specialist agents to extract scientific papers into reliable MCP servers with minimal manual setup
• You install the skill, point it at a paper URL and code repository, and it generates tested MCP tools ready to connect to your coding agent
• Three working examples (AlphaGenome, TISSUE, Scanpy) show agents handling genomic analysis, spatial transcriptomics, and single-cell preprocessing tasks with specific scientific queries