Create custom agents

Learn how to create custom agents, what to prepare before you start, and how to put them to work across messaging, decisioning, and data management. For more general information, see Braze Agents.

Prerequisites

Before you start, you’ll need the following:

• Access to the Agent Console in your workspace. Check with your Braze admins if you don’t see this option.
• Permission to create and edit custom AI Agents.
• An idea of what you want the agent to accomplish. Braze Agents can support the following actions:
  • Messaging: Generate subject lines, headlines, in-product copy, or other content.
  • Decisioning: Route users in Canvas based on behavior, preferences, or custom attributes.
  • Data management: Calculate values, enrich catalog entries, or refresh profile fields.

How it works

When you create an agent, you define its purpose and set guardrails for how it should behave. After it’s live, the agent can be deployed in Braze to generate personalized copy, make real-time decisions, or update catalog fields. You can pause or update an agent anytime from the dashboard.

The following use cases showcase a few ways to leverage custom agents.

Create an agent

Step 1: Set up details

To create your custom agent:

1. Go to Agent Console > Agent Management in the Braze dashboard.
2. Select Create agent.
3. Enter a name and description to help your team understand its purpose.
4. (optional) Add tags to filter your agent.
5. Select the interaction site, which is the location where the agent is deployed. Note that the interaction site can’t be updated after an agent is created.
6. Choose the model your agent will use.

Step 2: Write the instructions

Give the agent instructions. Refer to the Agents reference for guidance.

Step 2.1: Add context

Select Add context to choose what your agent can reference. This includes:

• Catalog fields: Provide catalog fields for the agent to reference.
• Segment membership: Consider a user’s membership in a segment when personalizing messages. You can select up to three segments.
• Brand guidelines: Reference the brand voice and style guidelines for the agent to follow. For example, if you want your agent to generate SMS copy to encourage users to sign up for a gym membership, you can use this field to reference your predefined bold, motivational guideline.
• All Canvas Context variables: Analyze all Canvas Context variables for a user when this agent is invoked, including any that are not referenced in the Instructions section.

Step 2.2: Add optional settings

In the Optional settings, you can adjust the temperature of the agent-generated copy. A higher temperature allows the agent to use the information provided to be more creative.

You can also set the daily execution limit for your agent. By default, this value is set to 50,000, but can be raised to 100,000. If you’re interested in increasing the limit above 100,000, contact your customer success manager to learn more.

Step 3: Select the output

In the Output section, you can organize and define the agent’s output by basic schemas or advanced schemas.

Basic schemas

Basic schemas are a simple output that an agent returns. This can be a string, a number, a boolean, an array of strings, or array of numbers.

Let’s say you want to collect user sentiment scores from a simple feedback survey to determine how satisfied your customers are after receiving a product. You can select Number as a basic schema to structure the output format.

Advanced schemas

Advanced schema options include manually structuring fields or using JSON.

• Fields: A no-code way to enforce an agent output that you can use consistently.
• JSON: A code approach to creating a precise output format, where you can nest variables and objects within the JSON schema.

• fields
• json schema

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{
  "type": "object",
  "properties": {
    "sentiment": {
      "type": "string"
    },
    "reasoning": {
      "type": "string"
    }
  },
  "required": [
    "sentiment",
    "reasoning"
  ]
}

Step 4: Test and create the agent

The Preview pane is an instance of the agent that shows up as a side-by-side panel within the configuration experience. You can use it to test the agent while you’re creating or making updates to it to experience it in a similar way to end users. This step helps you confirm that it’s behaving the way you expect, and gives you a chance to fine-tune before it goes live.

1. In the Test your agent field, enter example customer data or customer responses—anything that reflects real scenarios your agent will handle.
2. Preview the agent’s response for a random user, existing user, or custom user.
3. Select Simulate response. The agent will execute based on your configuration and display its response. Test runs count toward your daily execution limit.

Review the output with a critical eye. Consider the following questions:

• Does the copy feel on brand?
• Does the decision logic route customers as intended?
• Are the calculated values accurate?

If something feels off, update the agent’s configuration and test again. Run a few different inputs to see how the agent adapts across scenarios, especially edge cases like no data or invalid responses.

Step 5: Use and monitor your agent

Your agent is now ready to use! For details, refer to Deploy agents.

In the Logs tab of your agent, you can monitor actual agent calls that occur in your Canvases and catalogs. You can filter by information such as the date range, outcome (success or failure), or calling location.

Select View for a specific agent call to see the input, output, and user ID.