Building an Autonomous GPT Agent

Throughout this course, you have mastered the fundamentals of tool integration with GPT-5: creating tool schemas, understanding GPT-5's tool use responses, and executing single tool requests. However, the approach you've learned so far has a significant limitation — it only handles one tool call per conversation turn. While this works perfectly for simple tasks, many real-world problems require multiple sequential steps, and often the number and nature of these steps cannot be determined in advance.

In this lesson, we'll work together to transform GPT-5 from a single-turn tool user into an autonomous agent capable of iterative problem-solving. We'll build an agent class that can call tools, analyze results, decide what to do next, and continue this process until complex multi-step tasks are completed. This represents a fundamental shift from reactive tool usage to proactive, intelligent problem-solving that mirrors how humans approach complex challenges.

Before we start coding, let's understand how autonomous agents operate through action-feedback loops, in which each tool execution provides information that influences the next decision. This iterative process mirrors human problem-solving: we take an action, observe the result, decide what to do next, and repeat until we reach our goal. The action-feedback loop consists of four key phases that repeat until task completion:

1. Decision Phase: GPT-5 analyzes the current situation and determines the next action, which may include calling one or more tools.
2. Action Phase: Our agent executes the requested tool(s) based on GPT-5's instructions.
3. Feedback Phase: The results from the tool execution(s) are captured and added to the conversation history.
4. Evaluation Phase: GPT-5 reviews the new information, decides whether the task is complete or if additional steps are needed, and the loop continues.

This loop structure enables complex problem-solving because each iteration builds upon previous results. For example, when solving a quadratic equation, GPT-5 might first calculate the discriminant, then use that result to determine if real solutions exist, then calculate the square root of the discriminant, and finally compute the two solutions. The key insight is that GPT-5 doesn't need to plan all steps in advance — it can adapt its approach based on intermediate results, just like a human mathematician working through a problem.

Now let's start building our agent class to make this iterative process possible.