Detecting AI Agent Failure Modes in Simulations
AI agents have become significantly more common in the last few months. They’re used for web scraping,[1][2] robotics and automation,[3] and are even being deployed for military use.[4] As we integrate these agents into critical processes, it is important to simulate their behavior in low-risk environments. In this post, I’ll break down how I used Minecraft to discover and then resolve a failure in an AI agent system. By Michael Andrzejewski Dangers of AI Agents Let’s briefly discuss the concept of an AI agent, and how they differ from AI tools. An AI tool is a static interface like a chatbot or an image generator. A user enters some information and then receives a static response. For the tool to continue functioning, the user needs to enter more information. The tool can produce harmful outputs, such as misleading information for a chatbot or graphic images from an image generator. However, the danger here is pretty small and almost entirely dependent on the user. An AI agent is a recursive interface. A user gives it a task and executes an arbitrary sequence of actions to accomplish that task. The keyword here being ‘arbitrary’. Notice that AI agents are fundamentally a loop. Action Planning → Action Execution → Action Evaluation. This means that agents can accomplish many subtasks before stopping. By design, AI agents are more powerful and difficult to control than AI tools. A tool can analyze a specific X-ray for tumors; an agent can monitor a patient's condition, decide when new tests are needed, and adjust treatment plans. AI agents don’t require a human in the loop. Because of this additional freedom, agents can solve problems with more unique steps and accomplish more useful work. However, this also increases their potential for harm. Agent behavior can change as its token context increases,[5] causing it to become more biased over time. An agent tasked with managing patient care might begin acting as