Manage Deployments
Updated
You can manage existing AI deployments directly from the Record Manager screen in AI+ Studio. Click the vertical ellipsis (⋮) next to the desired deployment to access available actions.
Available Actions
Edit Pipeline: Modify the pipeline configuration for the selected deployment.
Edit Details: Update the basic details such as name, description, or tags.
Deploy / Undeploy: Activate or deactivate the deployment pipeline as needed.
View Audit Trail: Access detailed logs and performance metrics for the deployment.
Delete: Permanently delete the deployment from the system.
Audit Trail and Debug Logging
AI+ Studio provides Audit Trail and Debug Log Export capabilities for all configured deployments. This feature enables organizations to meet enterprise-grade observability, auditing, and compliance requirements.
Key Capabilities
View detailed logs of all deployment interactions.
Export logs for auditing, debugging, or compliance tracking.
Ensure transparency, traceability, and accountability across AI workflows.
Click the View Audit Trail button from vertical ellipsis (⋮) dropdown to access detailed logs. This opens the Audit Trails window.
The Audit Trails screen provides comprehensive insights into deployment activity within a specified time range. It displays both high-level summary metrics and detailed, request-level logs to help you monitor performance, identify issues, and ensure compliance.
The Audit Trail window is divided into three sections:
1. Summary Metrics
Metric | Description |
Total Requests | Total number of requests processed in the time frame. |
Total Success | Number of successfully completed requests. |
Total Failure | Number of requests that failed due to errors or Guardrails. |
Average Latency | Average response time per request (in seconds). |
P95 Latency | 95th percentile latency — time within which 95% of requests are completed. |
2. Requests
Each request is logged with the following attributes:
Date – Timestamp of when the request was made
Request ID – Unique identifier for the request
Latency – Time taken to complete the request
User Name – User who triggered the request
Case Number – Associated case number (if applicable)
Status – Outcome of the request (e.g., Success, Error)
3. Debug Logs
Detailed, timestamped logs of all node-level interactions within the deployment pipeline. This section includes:
Prompt Node Details – Input/output logs for each prompt
Final Output Information – The final AI-generated response
Error Messages – If applicable, detailed stack traces or blocking reasons
Export Audit Logs
To export logs from the Audit Trail:
Click the Export button at the top of the Audit Trail screen.
Choose the desired format: Excel (.xlsx) or JSON (.json).
A confirmation message will appear indicating that your export request has been submitted.
Once the export is complete, a download link will be sent to your Sprinklr Notification Center.
Click the link to download the report to your device.
AI+ Studio provides a centralized and efficient way to manage AI deployments. From the Record Manager screen, you can edit pipeline configurations, update deployment details, deploy or undeploy workflows, and remove unused deployments.
Use these capabilities regularly to maintain transparency, ensure accountability, and optimize the performance of your AI deployments.