AI Agent Safety

AI agent safety asks how to keep systems that plan over multiple steps and use tools, memory, or other agents within authorized and harmless bounds. Researchers test computer-use, coding, web, and embodied agents with prompt-injection, misuse, and long-horizon trajectory benchmarks, measuring executed effects as well as final answers. Approaches include red-teaming and monitoring, safety fine-tuning and refusal training, and runtime controls such as least-privilege access, pre-execution policy gates, sandboxing, human approval, and formal verification. A central challenge is that safety can fail through ambiguous goals, changing context, tool or agent interactions, and pressure over time, while stronger restrictions can block legitimate tasks and assurances may not transfer to deployment.