Integrating Boomi Agentstudio with Sage X3 Cloud Data via CData Connect AI

Boomi Agentstudio is an enterprise platform for designing, orchestrating, and governing AI agents that automate tasks, enhance integration workflows, and support intelligent decision making across business processes. When you connect it with CData Connect AI, Boomi Agentstudio can securely access, query, and act on live enterprise data such as Sage X3 Cloud through a standardized MCP tool interface.

CData Connect AI is a managed Model Context Protocol (MCP) platform that provides governed, real-time access to enterprise data systems. It exposes structured metadata, including catalogs, schemas, tables, and SQL querying across hundreds of data sources. With Connect AI, Boomi Agentstudio can incorporate live operational data directly into agent logic and workflow automation, eliminating the need for ETL pipelines, data replication, or custom integration code.

This article explains how to connect Boomi Agentstudio to a CData Connect AI MCP endpoint, configure access to your Sage X3 Cloud or any other supported data source, and begin issuing real-time queries from within your agent-driven workflows.

Prerequisites

• An account in Sage X3 Cloud.
• An account in CData Connect AI
• An account of Boomi Agentstudio.

Step 1: Configure Sage X3 Cloud connectivity for Boomi Agentstudio

For Boomi Agentstudio to access Sage X3 Cloud, create a connection to Sage X3 Cloud in CData Connect AI. This connection is then exposed to Boomi using the remote MCP server.

1. Log in to Connect AI click Sources, and then click + Add Connection
2. From the available data sources, choose Sage X3 Cloud
3. Enter the necessary authentication properties to connect to Sage X3 Cloud

   Sage X3 Cloud uses the OAuth 2.0 Client Credentials flow, and an X-API-Key is also required for API access. Set AuthScheme to OAuth and specify the following connection properties:

   • URL: The base URL of your Sage X3 Cloud instance.
   • OAuthAccessTokenUrl: The OAuth token endpoint (e.g., https://your-auth-domain/oauth/token).
   • OAuthClientId: Your OAuth application client ID.
   • OAuthClientSecret: Your OAuth application client secret.
   • Audience: The API audience value for the token request.
   • XAPIKey: The X-API-Key provided by your Sage X3 Cloud administrator.
   • Folder: The Sage X3 folder name (e.g., SEED). This folder is used as the default schema.
   • Folders (optional): A comma-separated list of Sage X3 folders (e.g., SEED,PERF). Each folder is exposed as a separate schema, so you can query across folders with the Schema.Table syntax.

   The driver obtains an access token with the Client Credentials flow and sends it with the X-API-Key on every API request. With InitiateOAuth set to GETANDREFRESH (the default), the driver acquires and refreshes the token automatically.

   
4. Click Create & Test
5. Once authenticated, open the Permissions tab in the Sage X3 Cloud connection and configure user-based permissions as required

Generate a Personal Access Token (PAT)

Boomi Agentstudio authenticates to Connect AI using an account email and a Personal Access Token (PAT). Creating separate PATs for each integration is recommended to maintain access control granularity.

1. In Connect AI, select the Gear icon in the top-right to open Settings
2. Under Access Tokens, select Create PAT
3. Provide a descriptive name for the token and select Create
4. Copy the token and store it securely. The PAT will only be visible during creation

With the Sage X3 Cloud connection configured and a PAT generated, Boomi Agentstudio is ready to connect to Sage X3 Cloud data via the CData Connect AI MCP server.

Step 2: Create a source using the CData Connect AI MCP endpoint

Start by creating a new MCP data source inside Boomi Agentstudio. This establishes a secure connection between Boomi and CData Connect AI, allowing agents to call MCP tools and work with live enterprise data.

To connect with Connect AI MCP as a source, follow the given process:

1. Log in to Boomi.
2. Open Services and select Agentstudio from the list.
3. Go to the Sources tab and click Create a new source.
4. In the Agent Designer window, open the Sources tab and choose Model Context Protocol (MCP) as the source type.
5. On the Create MCP Source screen, enter the following Configuration details:
   • Name: Provide a name for the source
   • Details: Add a short description for the source
   • Transport Type: Streamable HTTP
   • URL: https://mcp.cloud.cdata.com/mcp
   • Authentication: Basic Authentication
   • Username: Enter your Connect AI account username
   • Password: Enter your Connect AI PAT
6. Click Test Connection.
7. After you establish a successful connection, click Discover Tools. Boomi lists all MCP tools exposed by CData Connect AI, including queryData, getCatalogs, getSchemas, and getTables, along with the remaining tools, in the Tools tab.
8. Select all tools in the Discover and Select Tools section and click Continue.
9. In the Review section, verify the details and click Save.

Boomi adds the new source to the Sources tab.

Click the Tools tab to confirm that all tools from CData Connect AI appear in the list.

Step 3: Create a new agent

Create a new agent to interact with your Sage X3 Cloud data. The agent acts as the interface between your prompts and the tools exposed by Connect AI, enabling it to process queries and return intelligent responses.

1. Go to the Agents tab and click Create New Agent.
2. In the Agent Designer window, select Blank Template under the Agents tab.
3. In the Profile section, enter the following details:
   • Basic Information: Specify the goal, agent name, and agent picture.
   • Agent Mode: Select either Conversational or Structured mode based on how you want the agent to respond to prompts, and configure the mode accordingly.
4. Click Save and Continue.
5. In the Tasks section, define the actions your agent will perform:
   1. Click + Add New Task.
   2. In the Description tab, enter the task name and description.
   3. In the Instructions tab, click + Add New Instruction and describe how the agent should use the tool within this task.
   4. In the Tools tab, click + Add New Tool and select the tools exposed by Connect AI. Click Update Selected Tool, enable Requires Approval and Data Passthrough, save the task, and click Save and Continue.

   Note: You can add up to 25 tools across all tasks.

6. In the Guardrails section, define the rules, restrictions, and filters to ensure your agent operates securely and ethically. Add a blocked message, denied topics, word filters, and custom regex patterns as required. Click Save and Continue.
7. In the Review section, verify all details and click Deploy to deploy the agent.

After you deploy the agent, use it to generate accurate and contextual responses to your prompts in the chat interface.

Step 4: Prompt the Sage X3 Cloud data using the agent

After you create and deploy your agent, interact with your Sage X3 Cloud data using natural language prompts.

Follow these steps to prompt your Sage X3 Cloud data:

1. Go to the Chat tab and select your agent from the dropdown list.
2. Enter a prompt (for example, "How many tables are available in Sage X3 Cloud?").
3. The agent processes your prompt and returns the results.

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