Agentic Errors
Explore how to diagnose and resolve common causes of agentic errors in autonomous AI systems. Learn to identify planning mistakes, faulty tool usage, memory issues, and missing exit conditions. This lesson guides you through systematic debugging methods, including log inspection, stepwise analysis, and minimal reproducible cases, helping you ensure AI agents complete tasks reliably without looping indefinitely.
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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 ...
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 ...