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.

A strong answer will show that you understand why an agent might loop and how to systematically fix it. Let’s break down the common causes of looping behavior in agents and then outline a clear approach to debugging them, using our deep research example as a guiding scenario.

What are the common causes of looping or incomplete task execution?

When an AI agent keeps looping or can’t finish a job, it’s usually a symptom of an underlying issue in its reasoning or design. Here are the key issues that can cause an agent to loop or stall, along with examples and analogies to traditional software bugs.

Planning errors

Agents rely on their planning logic to break a high-level goal into smaller steps. The agent might go in circles if this plan is flawed or incomplete. For instance, the deep research agent might plan to search for a term, read an article, then search again in a slightly different way, and repeat without ever deciding to compile an answer. This is analogous to a bad algorithm in traditional software: imagine a function that calls itself recursively but never reaches a base case, or a loop with a wrong condition—it will run forever.

In an AI agent, a planning error could be as simple as a missing step (“check if answer is found”) or a wrong assumption (“if not found, retry search indefinitely”). The agent may not realize it’s stuck because its internal reasoning keeps telling it, “Not done yet, try something else,” even when “something else” is essentially the same action. A real-world parallel is a GPS that keeps rerouting you in a triangle because it misinterpreted the destination – it has a plan, but the plan is wrong. In debugging, recognizing a planning error involves examining how the agent breaks down the task: is it choosing actions that actually move toward completion, or is it repeatedly looping back over the same subtask?

Faulty tool usage

Many AI agents use external tools (like web search, databases, or APIs) to accomplish tasks. If the agent uses a tool incorrectly or encounters an error from the tool, it can lead to looping behavior. For example, our agent might call an API to get data, but perhaps it formats the query incorrectly or the API returns an error. A well-designed agent should handle that—maybe try a different approach or report failure—but a buggy one might not recognize the failure and simply call the same tool repeatedly. This is much like a piece of software calling a function that keeps throwing an exception but never catching it properly, ...