Agentic Errors

Imagine an advanced AI agent like ChatGPT in deep research mode, tasked with answering a complex question. This agent can break the problem into subtasks, use tools (web search, code execution, etc.), and iterate through a reasoning loop to arrive at an answer. For example, if asked about a recent research topic, the agent might search for relevant papers, summarize findings, and compile results. It’s designed to work autonomously, like an intern researcher who plans, acts, and learns in cycles until the task is done.

But what if something goes wrong? Suppose this deep research agent begins repeating the same search queries or cycling through the same actions without progressing. It might get stuck in a loop—endlessly fetching and reading data but never producing a final answer, or stalling midway through its plan. Such a scenario isn’t just hypothetical; AI researchers have observed agents that would occasionally get stuck in infinite reasoning loops that made no sense at all. In an interview setting, you might be asked how you would debug this exact problem: an AI agent that sometimes loops or fails to complete its tasks.