How to connect AI Agent and Microsoft SQL Server
Create a New Scenario to Connect AI Agent and Microsoft SQL Server
In the workspace, click the “Create New Scenario” button.

Add the First Step
Add the first node – a trigger that will initiate the scenario when it receives the required event. Triggers can be scheduled, called by a AI Agent, triggered by another scenario, or executed manually (for testing purposes). In most cases, AI Agent or Microsoft SQL Server will be your first step. To do this, click "Choose an app," find AI Agent or Microsoft SQL Server, and select the appropriate trigger to start the scenario.

Add the AI Agent Node
Select the AI Agent node from the app selection panel on the right.

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Configure the AI Agent
Click on the AI Agent node to configure it. You can modify the AI Agent URL and choose between DEV and PROD versions. You can also copy it for use in further automations.
Add the Microsoft SQL Server Node
Next, click the plus (+) icon on the AI Agent node, select Microsoft SQL Server from the list of available apps, and choose the action you need from the list of nodes within Microsoft SQL Server.

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Authenticate Microsoft SQL Server
Now, click the Microsoft SQL Server node and select the connection option. This can be an OAuth2 connection or an API key, which you can obtain in your Microsoft SQL Server settings. Authentication allows you to use Microsoft SQL Server through Latenode.
Configure the AI Agent and Microsoft SQL Server Nodes
Next, configure the nodes by filling in the required parameters according to your logic. Fields marked with a red asterisk (*) are mandatory.
Set Up the AI Agent and Microsoft SQL Server Integration
Use various Latenode nodes to transform data and enhance your integration:
- Branching: Create multiple branches within the scenario to handle complex logic.
- Merging: Combine different node branches into one, passing data through it.
- Plug n Play Nodes: Use nodes that don’t require account credentials.
- Ask AI: Use the GPT-powered option to add AI capabilities to any node.
- Wait: Set waiting times, either for intervals or until specific dates.
- Sub-scenarios (Nodules): Create sub-scenarios that are encapsulated in a single node.
- Iteration: Process arrays of data when needed.
- Code: Write custom code or ask our AI assistant to do it for you.

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Save and Activate the Scenario
After configuring AI Agent, Microsoft SQL Server, and any additional nodes, don’t forget to save the scenario and click "Deploy." Activating the scenario ensures it will run automatically whenever the trigger node receives input or a condition is met. By default, all newly created scenarios are deactivated.
Test the Scenario
Run the scenario by clicking “Run once” and triggering an event to check if the AI Agent and Microsoft SQL Server integration works as expected. Depending on your setup, data should flow between AI Agent and Microsoft SQL Server (or vice versa). Easily troubleshoot the scenario by reviewing the execution history to identify and fix any issues.
Most powerful ways to connect AI Agent and Microsoft SQL Server
AI Agent + Microsoft SQL Server + Slack: The AI Agent processes customer queries. If the AI Agent cannot resolve an issue, the details are logged into Microsoft SQL Server. Then, Slack sends a message to a designated channel to alert the support team about the unresolved issue.
Microsoft SQL Server + AI Agent + Jira: When a new or updated row is detected in Microsoft SQL Server based on a custom query (alert condition), the AI Agent summarizes the issue from the data. Jira then creates a new issue based on the AI summary for the IT support team.
AI Agent and Microsoft SQL Server integration alternatives
About AI Agent
Use AI Agent in Latenode to automate content creation, data analysis, or customer support. Configure agents with prompts, then integrate them into workflows. Unlike standalone solutions, Latenode lets you connect AI to any app, scale automatically, and customize with code where needed.
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About Microsoft SQL Server
Use Microsoft SQL Server in Latenode to automate database tasks. Directly query, update, or insert data in response to triggers. Sync SQL data with other apps; simplify data pipelines for reporting and analytics. Build automated workflows without complex coding to manage databases efficiently and scale operations.
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FAQ AI Agent and Microsoft SQL Server
How can I connect my AI Agent account to Microsoft SQL Server using Latenode?
To connect your AI Agent account to Microsoft SQL Server on Latenode, follow these steps:
- Sign in to your Latenode account.
- Navigate to the integrations section.
- Select AI Agent and click on "Connect".
- Authenticate your AI Agent and Microsoft SQL Server accounts by providing the necessary permissions.
- Once connected, you can create workflows using both apps.
Can I analyze customer feedback stored in SQL using AI?
Yes, you can! Latenode simplifies this by connecting AI Agent to your database. Analyze feedback, identify trends, and improve customer satisfaction, all within a single workflow.
What types of tasks can I perform by integrating AI Agent with Microsoft SQL Server?
Integrating AI Agent with Microsoft SQL Server allows you to perform various tasks, including:
- Automatically generate SQL queries based on natural language input.
- Enrich database records with AI-driven sentiment analysis.
- Create AI-powered chatbots that access real-time database information.
- Automate report generation with AI-summarized data insights.
- Monitor database activity and trigger alerts based on AI-detected anomalies.
How easily can I scale my AI Agent automations on Latenode?
Latenode's architecture allows scaling AI Agent automations easily. Upgrade your plan to increase processing power without code changes.
Are there any limitations to the AI Agent and Microsoft SQL Server integration on Latenode?
While the integration is powerful, there are certain limitations to be aware of:
- Complex SQL queries may require optimization for optimal performance.
- AI Agent's processing is subject to rate limits based on your chosen plan.
- Initial setup requires a basic understanding of database structures.