  A prerequisite for safe autonomy-in-the-wild is safe testing-in-the-wild. Yet
real-world autonomous tests face several unique safety challenges, both due to
the possibility of causing harm during a test, as well as the risk of
encountering new unsafe agent behavior through interactions with real-world and
potentially malicious actors. We propose a framework for conducting safe
autonomous agent tests on the open internet: agent actions are audited by a
context-sensitive monitor that enforces a stringent safety boundary to stop an
unsafe test, with suspect behavior ranked and logged to be examined by humans.
We design a basic safety monitor (AgentMonitor) that is flexible enough to
monitor existing LLM agents, and, using an adversarial simulated agent, we
measure its ability to identify and stop unsafe situations. Then we apply the
AgentMonitor on a battery of real-world tests of AutoGPT, and we identify
several limitations and challenges that will face the creation of safe
in-the-wild tests as autonomous agents grow more capable.
