Welcome to the Agent Architecture Platform Guide. This guide provides detailed instructions on how to create, configure, and deploy AI Agents in our platform to enhance your workflows. AI Agents are versatile tools that can assist with various tasks, such as answering questions, performing actions, and interacting with users through channels like Microsoft Teams or phone calls. The primary use case is L1 support, but Agents can be adapted to any workflow where automation and AI assistance are beneficial.This guide is divided into three main sections:
Agent Platform – Creating and configuring Agents.
Tools – Configuring tools that Agents can use to perform actions.
Deploying Agents into Workflows – Integrating Agents into workflows and external channels.
Each section includes step-by-step instructions and examples to help you understand when and how to use each feature.
AI Agents are created and configured within the context of an App’s workflow. Follow these steps to create a new Agent:
Navigate to App Configuration:
Go to the App where you want to add the Agent.
Access the workflow configuration section.
Add an Agent:
Select the option to add an Agent to the workflow.
You can view existing Agents assigned to the App or create a new one.
Create a New Agent:
Name the Agent: Provide a unique name for the Agent.
Add a Description: Describe how the Agent fits into your organization and its intended use case.
Select the LLM Model: Choose the Large Language Model (LLM) to power the Agent (e.g., Claude Sonnet 3.5 or 3.7 from Snowflake Cortex, if configured).
If no models are available, follow the link to set up a new AI Service and Provider.
Enter Training Instructions: Provide additional instructions to guide the Agent’s behavior.
Start a Conversation: Test the Agent by starting a conversation. You can clear the conversation and start a new one if needed.
Save the Agent: Once configured, save the Agent to create its profile.
View and Edit Agent Profile:
From the Agent profile, you can edit the configuration or start a new conversation to further test the Agent.
Example Use Case:
L1 Support: Create an Agent named “SupportBot” with a description like “Handles initial customer queries and creates tickets.” Use Claude Sonnet 3.5 and train it to ask users for issue details.
The Create Record Tool allows the Agent to create new records in the system.Configuration Steps:
Add the Tool: When configuring an Agent, select the option to add a Create Record Tool.
Name the Tool: Provide a unique name for the tool.
Add a Description: Describe when and how the Agent should use this tool.
Select App/Element/Task: Choose the specific App, Element, or Task that the Agent can create.
Specify Fields:
Required Fields: These are shown by default and cannot be removed.
Optional Fields: Add any additional fields the Agent may need to fill out.
Field Descriptions: For each field, provide a description to guide the Agent on how to obtain the necessary data.
Select Fields to Return: Choose which fields from the created record should be sent back to the Agent.
Save the Tool: Once all fields have descriptions, save the tool.
Example Use Case:
L1 Support: Configure the tool to create a support ticket with fields like “Issue Description” (required) and “Priority” (optional). The Agent asks the user for details and returns the ticket ID.
The AI Search Records Tool provides advanced search capabilities using AI, allowing the Agent to understand and retrieve related data based on natural language.Configuration Steps:
Add the Tool: Select the option to add an AI Search Records Tool.
Name the Tool: Provide a unique name.
Add a Description: Explain when the Agent should use this tool.
Select App/Element/Task: Choose the App, Element, or Task to search.
Set Result Limit: Specify the maximum number of results to return (1-100, default is 10).
Configure AI Search:
Select the AI Search configuration for the object.
Choose attribute fields to search on and set up any necessary filters.
Provide descriptions for each field to guide the Agent.
Select Fields to Return: Choose which fields from the search results should be sent back to the Agent.
Save the Tool: Once configured, save the tool.
Example Use Case:
Sales Assistance: The Agent uses AI search to find customer records with a query like “Customers interested in Product X” and returns contact details.
This guide has provided a comprehensive overview of how to create, configure, and deploy AI Agents in your platform. By following these instructions, you can leverage Agents to automate tasks, assist users, and streamline workflows. Always test your Agents thoroughly to ensure they meet your specific use cases, whether for L1 support or other scenarios like HR onboarding or sales assistance.For further assistance, refer to the knowledge base or contact support.Note: This guide is based on the current feature set as of June 13, 2025. Features not mentioned are not supported at this time.