Agent Safety

Agent safety refers to the work of keeping AI agents, which are systems that take actions on their own such as running code, sending messages, or changing records, from causing unintended harm. For enterprise leaders, it matters because an agent that acts without adequate limits can expose the organization to data loss, unauthorized transactions, and liability for decisions no human reviewed. Coverage under this tag typically tracks the controls organizations use to constrain agent permissions, require human approval for high-risk steps, and monitor what agents actually do.

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ResearchUS2026-10-21

Agents With Spending Authority Need Behavioral Testing First, Research Warns

A working paper from the Real Safety AI Foundation warns that autonomous AI agents may behave unpredictably when given financial authority or placed under resource pressure. The paper recommends that organizations require foundation model disclosure, conduct behavioral testing, apply spending controls, and run structured red-teaming before any agent receives financial permissions. The findings affect model risk management programs and third-party AI oversight in any sector where agents are authorized to spend money.

agentic AI financial controls red teaming model risk management agent safety autonomous agents Real Safety AI Foundation