The Agent Builder is a tool that lets you design, create, and organize agents to automate your processes in Glean. Think of the Agent Builder as your “workshop” for building automations where you decide what your agent will do, step by step.

Using the Agent Builder, you can break down complex work into clear, manageable instructions. Each agent you create follows a series of steps that you set up, like collecting information, making decisions, or running Sub-agents.

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Agent Settings

You can find Agent settings by clicking the gear icon at the top right in the Agent Builder screen. All agents require an icon and a name. You can help your teammates understand what your agent does by adding a description. This is what your teammates will see when they browse through the agent library.

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Agent Models

The Glean Model Hub enables you to experiment with and select the most suitable model for each agent and its respective steps. This selection of models allows organizations to flexibly experiment with, choose, and combine leading AI models for each step of their workflows, ensuring data safety, rapid access to new models, and robust performance tracking.

It also empowers businesses to optimize AI performance and cost without vendor lock-in or management overhead. For more information on accessing the model hub, please refer to the Set up LLMs article.

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Agent goal message

The agent goal message is intended to clarify the specific objective that the agent aims to achieve during interactions. It serves as a short description that helps the AI to provide more focused and relevant responses based on the task at hand.

When writing an agent goal message you should consider:

  • Clarity and Specificity: Clearly state what the agent is supposed to achieve. Avoid vague or broad goals, as this can make it harder for the LLM to provide relevant responses.
  • Conciseness: The goal message should be short but comprehensive, summarizing the purpose of the conversation or task.
  • User Perspective: The message should make sense to both the AI and the user, providing enough detail to guide the interaction without being confusing.
  • Distinguishability: If there are multiple agents or similar conversations, craft the goal message to help differentiate between them.

Here are some examples of agent goals across a few job functions:

  • Engineering: “Assist developers in debugging code and provide solutions for common programming errors.”
  • Sales: “Guide prospects through product features, answer pricing questions, and assist with purchase decisions.”
  • Support: “Help users troubleshoot technical issues and deliver step-by-step resolutions for reported problems.”

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Preview

The preview agent option lets creators test or interact with their agent in a safe, private environment before making it available to a wider audience. This allows for troubleshooting, refining agent responses, and validating functionality without impacting real users or live workflows. This also streamlines the development cycle by enabling iterative updates and immediate feedback during testing.

After you kick off your agent on this screen, you will have two additional options: ‘Reset Preview’ or ‘Try Again’.

  • Reset Preview This will send you back to the first screen to enter in new values for the first trigger.
  • Try Again: This will run the last query you entered so you can see how the LLM might vary in responses.

The Agent Builder is a tool that lets you design, create, and organize agents to automate your processes in Glean. Think of the Agent Builder as your “workshop” for building automations where you decide what your agent will do, step by step.

Using the Agent Builder, you can break down complex work into clear, manageable instructions. Each agent you create follows a series of steps that you set up, like collecting information, making decisions, or running Sub-agents.

​
Agent Settings

You can find Agent settings by clicking the gear icon at the top right in the Agent Builder screen. All agents require an icon and a name. You can help your teammates understand what your agent does by adding a description. This is what your teammates will see when they browse through the agent library.

​
Agent Models

The Glean Model Hub enables you to experiment with and select the most suitable model for each agent and its respective steps. This selection of models allows organizations to flexibly experiment with, choose, and combine leading AI models for each step of their workflows, ensuring data safety, rapid access to new models, and robust performance tracking.

It also empowers businesses to optimize AI performance and cost without vendor lock-in or management overhead. For more information on accessing the model hub, please refer to the Set up LLMs article.

​
Agent goal message

The agent goal message is intended to clarify the specific objective that the agent aims to achieve during interactions. It serves as a short description that helps the AI to provide more focused and relevant responses based on the task at hand.

When writing an agent goal message you should consider:

  • Clarity and Specificity: Clearly state what the agent is supposed to achieve. Avoid vague or broad goals, as this can make it harder for the LLM to provide relevant responses.
  • Conciseness: The goal message should be short but comprehensive, summarizing the purpose of the conversation or task.
  • User Perspective: The message should make sense to both the AI and the user, providing enough detail to guide the interaction without being confusing.
  • Distinguishability: If there are multiple agents or similar conversations, craft the goal message to help differentiate between them.

Here are some examples of agent goals across a few job functions:

  • Engineering: “Assist developers in debugging code and provide solutions for common programming errors.”
  • Sales: “Guide prospects through product features, answer pricing questions, and assist with purchase decisions.”
  • Support: “Help users troubleshoot technical issues and deliver step-by-step resolutions for reported problems.”

​
Preview

The preview agent option lets creators test or interact with their agent in a safe, private environment before making it available to a wider audience. This allows for troubleshooting, refining agent responses, and validating functionality without impacting real users or live workflows. This also streamlines the development cycle by enabling iterative updates and immediate feedback during testing.

After you kick off your agent on this screen, you will have two additional options: ‘Reset Preview’ or ‘Try Again’.

  • Reset Preview This will send you back to the first screen to enter in new values for the first trigger.
  • Try Again: This will run the last query you entered so you can see how the LLM might vary in responses.