Instead of treating agent safety controls as static, you can automatically improve them by analyzing what goes wrong in practice and evolving specific components like rules and tool permissions—achieving 3x better safety without sacrificing utility.
This paper introduces SHE, a framework that automatically improves LLM agent safety by evolving the agent's control system (harness) rather than just the model weights. It breaks the harness into four modular components—system prompt, rules, memory, and tool access—and learns safer versions of each from failure examples, while maintaining the agent's usefulness.