How To Use OpenAI Agent Builder For Beginners

This video offers a beginner-friendly guide to OpenAI's agent builder, breaking down its core components: the agent builder itself, chat kit for integration, and widgets for UI. While presented with a drag-and-drop interface, the speaker emphasizes its developer-centric nature, requiring more than simple block-and-action logic. The tutorial walks through creating a workflow, illustrating how to set up an initial "agent" to identify if a user's query requires internet access, using a true/false boolean for decision-making.

The demonstration shows how this boolean logic directs the workflow. If internet access is needed, the workflow proceeds to a web search agent; otherwise, it follows an alternative path, such as providing a coffee joke. The video also touches upon integrating widgets, explaining that they provide a structured UI output, but notes that strict adherence to widget format is crucial to avoid workflow breakage, especially in beta stages where errors are common. The speaker also mentions future in-depth tutorials on chat kit and app builder features.

• The OpenAI agent builder consists of the agent builder interface, chat kit for website/software integration, and widgets for chatbot UI.
• Workflows are initiated with a "chat" trigger and utilize various tools like agents, end points, notes, file search, guard rails, MCP, if/else statements, loops, user approval, transform, and set state.
• Agents are the core AI components, defining prompts, models, reasoning, and tools (e.g., web search, code interpreter).
• A practical example demonstrates setting up an agent to determine if a user's query requires internet access using a boolean (true/false) output.
• Widgets are explained as UI elements for chatbot responses, with a warning that strict output formatting is necessary to prevent workflow failures, especially in the beta phase.

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The Agent Builder Ecosystem [00:25]

• The agent builder is found within your OpenAI developer account and allows for creating workflows.
• Chat kit is a fundamental part of agent workflows, enabling easy embedding into websites or software.
• Widgets define the user interface (UI) for chatbot responses.
• The drag-and-drop UI for workflow creation is noted as being developer-heavy, requiring more than basic block manipulation.

Even though this looks drag and drop like, "Oh, Corbin, this should be really easy." This is developer heavy. So, that's partly why you're probably watching this video because you're going to have to use a little bit more code syntax than you would just like, "Oh, this is a Zapier video. Let me just drag a block and do an action."

Core Workflow Components [01:46]

• Workflows are initiated by a chat trigger, for example, by saying "hello."
• Agents are the AI integrated into the workflow, encompassing prompts, models, reasoning, and tools like web search, file search, and code interpreter. Agents are considered the fundamental building blocks of workflows.
• The "End" component stops the agent and the workflow.
• Workflows can theoretically loop, maintaining context through "workflow data."
• "Notes" are comments within the workflow that do not affect its execution.
• "File search" allows the AI to reference files stored in a vector database, capable of handling PDFs.
• "Guard rails" are preset components for moderation, acting like pre-tuned if-else statements to prevent undesirable outputs (e.g., hate speech).
• "MCP" allows integration with various apps and is noted as being complex and requiring a separate tutorial.
• "If else" statements are classic development constructs that execute different actions based on a true or false condition.
• "While" loops allow for repeatedly performing an action a specified number of times, with a caution about infinite loops costing credits.
• "User approval" acts as an if-else statement that requires user consent before proceeding.
• "Transform" is a crucial block for converting data into a readable format for the next block.
• "Set state" saves relevant data from a trigger that can be referenced later in the flow.

Agents are going to be essentially the fundamental laying bricks for all workflows because without agents, none of this really matters. That's the AI. That's that's why we like this. That's the AI.

Building a Workflow: If-Else Logic and Variables [05:47]

• The demonstration focuses on setting up if-else logic and variables within an agent workflow.
• An agent is set up with the purpose of acting as an identifier for a boolean (true/false) output.
• This agent is named "is web search" and instructed to set "is web" to true if the user message requires internet access.
• The output format is set to JSON, described as a coding language for AI readability.
• A property named "is web" of type "bool" (true or false) is defined.
• The "web search" tool is added to the agent, and the "requested message" is set as context input.

Workflow Execution and Evaluation [07:13]

• The workflow is connected, with the "is web" boolean linked to an "if else" block.
• The condition is set as "if is web == true."
• If the initial agent determines internet search is needed, it sets "is web" to true, triggering the subsequent action.
• The "evaluate" tab serves as an error log or console log for developers, showing workflow execution details and responses.
• The evaluation confirms that the AI identified the need for internet access to determine weather data.
• If "is web" is true, the workflow routes to a specific path; otherwise (else), it takes another path.
• More complex conditional logic can be implemented with "else if" statements.

So, then the output was only is web true. Perfect. That is the only output we're going to read in the next step.

Implementing Web Search and Alternative Paths [09:33]

• When "is web search" is true, a new agent with the "web search" tool is connected.
• This agent uses the "requested message" as input.
• A second agent named "coffee joke" is created with the objective of telling a coffee joke.
• The workflow is tested with two scenarios: one requiring web search (weather query) and one not (capital of Texas query).
• When asked about the weather, the workflow correctly identifies the need for web search and retrieves weather information.
• When asked for the capital of Texas, which doesn't require internet search, the workflow routes to the "coffee joke" agent.
• The speaker notes a slight deviation where the "capital of Texas" query directly provided the answer, bypassing the coffee joke, but the principle of the if-else logic directing to the "false" path remains.

Boom. Austin. I guess it went quite direct here, though. All right, so fair enough. I guess it wanted to go quite direct there. It ignored the coffee joke, but you get the idea.

Integrating Widgets and Handling Errors [11:18]

• Widgets are integrated into the workflow to provide UI outputs instead of plain text.
• The speaker downloads a pre-made weather widget.
• Creating custom widgets can be done via an "app builder," which is slated for a separate video due to its complexity and potential errors.
• Uploading a widget constrains the AI agent's output to that specific widget format.
• If the output deviates from the expected widget format, the workflow will break.
• A demonstration shows the widget working for a weather query, albeit with some missing elements.
• When a query like "look up best ways to cook chicken" is made, the widget output breaks entirely because the output is not in the expected weather widget format.

But the idea here is that then that means when creating these workflows, you're going to have to get quite traditional on the different types of agents and the different types of widgets you want to output.

Publishing and Next Steps [14:20]

• Workflows are published with a given name and version.
• Chat kit's functionality and integration into applications or websites will be covered in separate, in-depth videos, including creating a GitHub code repository and open-sourcing a starter template.
• Future topics will include integrating third-party APIs.
• The speaker asks for community input on which topics to prioritize next, mentioning planned videos on Zapier and MCP integrations.
• The speaker also promotes daily streams on Twitch covering AI news and community content.

So, there we go. Let me know in the comments what you want me to prioritize next with these AI agent builders. Because to be honest with you, I'm on a schedule where I post one video a day, but even then, I've already wrote out like six videos I need to do just on this topic alone.