Google Vertex AI and Jira Integration

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Automatically analyze Jira tickets with Google Vertex AI to prioritize high-impact issues. Latenode’s visual editor and affordable execution-based pricing simplify AI integration, while JavaScript support enables custom data enrichment for smarter workflows.

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Google Vertex AI

Jira

Step 1: Choose a Trigger

Step 2: Choose an Action

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How to connect Google Vertex AI and Jira

Create a New Scenario to Connect Google Vertex AI and Jira

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 Google Vertex AI, triggered by another scenario, or executed manually (for testing purposes). In most cases, Google Vertex AI or Jira will be your first step. To do this, click "Choose an app," find Google Vertex AI or Jira, and select the appropriate trigger to start the scenario.

Add the Google Vertex AI Node

Select the Google Vertex AI node from the app selection panel on the right.

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Google Vertex AI

Configure the Google Vertex AI

Click on the Google Vertex AI node to configure it. You can modify the Google Vertex AI URL and choose between DEV and PROD versions. You can also copy it for use in further automations.

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Run node once

Add the Jira Node

Next, click the plus (+) icon on the Google Vertex AI node, select Jira from the list of available apps, and choose the action you need from the list of nodes within Jira.

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Authenticate Jira

Now, click the Jira node and select the connection option. This can be an OAuth2 connection or an API key, which you can obtain in your Jira settings. Authentication allows you to use Jira through Latenode.

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Configure the Google Vertex AI and Jira Nodes

Next, configure the nodes by filling in the required parameters according to your logic. Fields marked with a red asterisk (*) are mandatory.

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Run node once

Set Up the Google Vertex AI and Jira 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 Google Vertex AI, Jira, 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 Google Vertex AI and Jira integration works as expected. Depending on your setup, data should flow between Google Vertex AI and Jira (or vice versa). Easily troubleshoot the scenario by reviewing the execution history to identify and fix any issues.

Most powerful ways to connect Google Vertex AI and Jira

Jira + Google Vertex AI + Slack: When a new issue is created in Jira, use Google Vertex AI to analyze the issue description and determine its urgency. If the AI identifies the issue as urgent, send a message to a designated Slack channel to alert the relevant team.

Jira + Google Vertex AI + Google Sheets: When a Jira issue is updated, use Google Vertex AI to summarize the resolution. Then, add a new row to a Google Sheet containing the issue key, resolution summary, and update timestamp, to track resolution trends.

Google Vertex AI and Jira integration alternatives

About Google Vertex AI

Use Vertex AI in Latenode to build AI-powered automation. Quickly integrate machine learning models for tasks like sentiment analysis or image recognition. Automate data enrichment or content moderation workflows without complex coding. Latenode’s visual editor makes it easier to chain AI tasks and scale them reliably, paying only for the execution time of each flow.

About Jira

Sync Jira issues to other tools or trigger actions based on status changes. Automate bug reporting, task assignment, or notifications without code. Latenode lets you visually integrate Jira into complex workflows. Extend functionality with JavaScript and control costs with execution-based pricing, not per-step fees.

See how Latenode works

FAQ Google Vertex AI and Jira

How can I connect my Google Vertex AI account to Jira using Latenode?

To connect your Google Vertex AI account to Jira on Latenode, follow these steps:

  • Sign in to your Latenode account.
  • Navigate to the integrations section.
  • Select Google Vertex AI and click on "Connect".
  • Authenticate your Google Vertex AI and Jira accounts by providing the necessary permissions.
  • Once connected, you can create workflows using both apps.

Can I automatically create Jira tickets from AI-analyzed customer feedback?

Yes, you can! Latenode's visual editor simplifies automating ticket creation based on sentiment analysis from Google Vertex AI, improving response times and issue tracking.

What types of tasks can I perform by integrating Google Vertex AI with Jira?

Integrating Google Vertex AI with Jira allows you to perform various tasks, including:

  • Automatically triage support tickets based on AI-detected urgency.
  • Enrich Jira issues with AI-generated summaries of customer conversations.
  • Predict resolution times for Jira tickets using machine learning models.
  • Analyze bug reports in Jira to identify common patterns and root causes.
  • Automatically assign Jira tickets to specialists based on AI topic detection.

How easily can I pass data between Google Vertex AI and Jira?

Latenode offers a visual interface for mapping data fields between Google Vertex AI and Jira, ensuring seamless data transfer and reducing manual input.

Are there any limitations to the Google Vertex AI and Jira integration on Latenode?

While the integration is powerful, there are certain limitations to be aware of:

  • Rate limits imposed by Google Vertex AI and Jira APIs may affect performance.
  • Complex data transformations may require custom JavaScript code.
  • Large file transfers may experience delays depending on network conditions.

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