Quick Agent Builder Tutorial: Immediate Results

At OpenAI DevDay 2025, engineer Christina Huang demonstrated the power of Agent Builder by creating an entire AI workflow and two AI agents live onstage in under eight minutes. This tutorial recreates that experience, walking you through building your first agent from scratch.

Prerequisites

You'll need an OpenAI API account with access to AgentKit. Navigate to the Agent Builder interface in your OpenAI dashboard. Have your use case in mind—for this tutorial, we'll build a customer service bot.

Minute 1-2: Choosing a Template

Start by selecting the "Customer Service Bot" template from the Agent Builder gallery. This provides a foundation with common customer service workflows already configured.

The template includes greeting logic, intent classification, and basic response generation. We'll customize this for a fictional e-commerce company handling order status inquiries.

Click "Add Connector" and select your database or API endpoint. For this example, we'll connect to an orders database using a REST API connector.

Minute 3-4: Adding Data Sources

Configure the connector with your API endpoint, authentication method, and expected response format. Agent Builder validates the connection in real-time, showing you sample data.

Drag a "Fetch Data" node onto the canvas and connect it to the intent classification node. Configure it to query order status based on the customer's input (order number or email).

Add an "If-Else" node to handle different scenarios: order found vs. not found, order shipped vs. processing, and issues requiring human escalation.

Minute 5-6: Adding Logic and Guardrails

For each branch, add response generation nodes with appropriate messaging. Include an escalation path that creates a support ticket and notifies your team for complex cases.

Add a PII detection guardrail to ensure the agent doesn't leak sensitive customer information like credit card numbers or passwords.

Minute 7: Testing

Use Agent Builder's built-in test panel to simulate customer interactions. Try various scenarios: valid order numbers, invalid order numbers, ambiguous queries, and attempts to extract sensitive information.

Minute 8: Deployment

The visual debugger shows you exactly how data flows through your agent and where decisions are made. Adjust logic as needed based on test results.

Once testing looks good, click "Deploy." Choose your deployment target—for this tutorial, we'll create an API endpoint.

Agent Builder generates the necessary infrastructure and provides you with an endpoint URL and authentication credentials. Your agent is now live and ready to handle real customer inquiries.

Beyond the Basics

Now that you've built your first agent in 8 minutes, consider these enhancements: add sentiment analysis to detect frustrated customers, implement feedback collection after each interaction, connect to additional data sources (shipping, inventory), set up monitoring and alerts for common failure modes, and create variants for A/B testing different approaches.

Best Practices Learned

Start with templates when available—they incorporate OpenAI's best practices. Test edge cases early and often. Implement escalation paths for scenarios you can't automate. Use guardrails liberally to prevent unwanted behavior. Monitor production performance and iterate continuously.