Build Agents in Relevance AI with Access to Live SAS xpt Data via CData Connect AI

Relevance AI is an AI automation and agent-building platform that enables organizations to create autonomous workflows powered by natural language reasoning. Users can visually design agents that interact with APIs, databases, and third-party systems to complete everyday business tasks or data operations.

By integrating Relevance AI with CData Connect AI through the built-in MCP (Model Context Protocol) Server, your agents can query, summarize, and act on live SAS xpt data in real time. This connection bridges Relevance AI intelligent workflow engine with the governed enterprise connectivity of CData Connect AI ensuring every query runs securely against authorized sources without manual data export.

This article outlines the steps to configure SAS xpt connectivity in Connect AI, register Connect AI in Relevance AI, and build an agent that interacts with live SAS xpt data.

Step 1: Configure SAS xpt Connectivity for Relevance AI

Connectivity to SAS xpt from Relevance AI is made possible through CData Connect AI's Remote MCP Server. To interact with SAS xpt data from Relevance AI, we start by creating and configuring a SAS xpt connection in CData Connect AI.

1. Log into Connect AI, click Sources, and then click Add Connection

2. Select SAS xpt from the Add Connection panel

3. Enter the necessary authentication properties to connect to SAS xpt.

   Connecting to Local SASXpt Files

   You can connect to local SASXpt file by setting the URI to a folder containing SASXpt files.

   Connecting to S3 data source

   You can connect to Amazon S3 source to read SASXpt files. Set the following properties to connect:

   • URI: Set this to the folder within your bucket that you would like to connect to.
   • AWSAccessKey: Set this to your AWS account access key.
   • AWSSecretKey: Set this to your AWS account secret key.
   • TemporaryLocalFolder: Set this to the path, or URI, to the folder that is used to temporarily download SASXpt file(s).

   Connecting to Azure Data Lake Storage Gen2

   You can connect to ADLS Gen2 to read SASXpt files. Set the following properties to connect:

   • URI: Set this to the name of the file system and the name of the folder which contacts your SASXpt files.
   • AzureAccount: Set this to the name of the Azure Data Lake storage account.
   • AzureAccessKey: Set this to our Azure DataLakeStore Gen 2 storage account access key.
   • TemporaryLocalFolder: Set this to the path, or URI, to the folder that is used to temporarily download SASXpt file(s).
   
4. Click Save & Test
5. Navigate to the Permissions tab and update user-based permissions

Add a Personal Access Token

A Personal Access Token (PAT) is used to authenticate the connection to Connect AI from Relevance AI. It is best practice to create a separate PAT for each integration to maintain granular access control.

1. Click the gear icon () at the top right of the Connect AI app to open Settings
2. On the Settings page, go to the Access Tokens section and click Create PAT
3. Give the PAT a descriptive name and click Create

4. Copy the token when displayed and store it securely. It will not be shown again

With the SAS xpt connection configured and a PAT generated, Relevance AI can now connect to SAS xpt data through Connect AI.

Step 2: Configure Connectivity in Relevance AI

The CData Connect AI MCP endpoint and authorization details are registered within Relevance AI so that agents can call live data from Connect AI.

1. Sign in to Relevance AI and create an account if you do not already have one
2. From the sidebar, navigate to Agents and then click on New Agent

3. Select Build from scratch and name the agent (eg; CData MCP Server)

4. Inside the agent editor, select Advanced and then switch to the MCP Server tab

5. Click + Add Remote MCP Tools
6. In the dialog that appears, fill out the fields as follows:
• URL: https://mcp.cloud.cdata.com/mcp
• Label: Any custom label (eg; cdata_mcp_server)
• Authentication: Select Custom headers
• Add header key:value pair. Combine your email and PAT as email:PAT and encode that string in Base64 and then prefix with the word Basic
  • Key: Authorization
  • Value: Basic base64(email:PAT)

Click Connect to establish the connection. Relevance AI will verify your credentials and register the CData Connect AI MCP Server for use in agents.

Step 3: Build and Run a Relevance AI Agent with Live SAS xpt Data

1. Switch to the Run tab for your agent
2. Enter a task for example, "List the five most recent incidents from ServiceNow"

3. The agent will query Connect AI via the MCP endpoint and display live results from SAS xpt data

With the connection complete, Relevance AI agents can now issue queries, retrieve records, and perform AI-driven tasks over live SAS xpt data through CData Connect AI MCP Server.

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