Leveraging OpenAI Hosted Tools

Welcome to your next step in mastering OpenAI agents! In the previous course, you learned how to coordinate multiple agents and customize how they hand off tasks to each other. You practiced enforcing input validation and running custom logic during these handoffs, making your agent workflows more robust and reliable.

Now, you are ready to expand your agents' abilities even further. In this lesson, you will learn how to give your agents access to real-time information and external data by integrating OpenAI hosted tools. This is a major step forward: while agents are already powerful at reasoning and following instructions, their knowledge is limited to what was available at the time their model was trained. Tools, such as the OpenAI hosted webSearchTool, allow your agents to reach out to the web and other sources, making them much more useful for real-world tasks that require up-to-date information.

By the end of this lesson, you will know how to equip your agents with hosted tools and see how this changes what they can do. Let's get started by understanding what hosted tools are and why they matter.

While LLMs can reason and generate text, they are limited not only in their knowledge, which is restricted to their training data, but also in their ability to take action. By connecting to tools, agents can go beyond these limitations—taking real action by accessing real-time information, performing calculations, or interacting with external systems to accomplish tasks.

Here are some of the types of tools agents can use:

• Built-in tools: These come with the agent library or platform.
• Hosted tools: Tools provided and managed by third parties, such as OpenAI.
• Custom tools: Tools you create yourself, allowing agents to connect to your own APIs, databases, or business logic.

When you give an agent access to tools, you list them in its configuration. The agent then decides, based on the user's request and its own reasoning, when and how to use each tool. It can call a tool, use the result, and combine that information with its own knowledge to generate a final answer.

This ability to use tools is what transforms an agent from a static LLM into a dynamic assistant that can solve real-world problems and provide up-to-date, actionable information.