Release Update 12/22/2025

Platform

Expanded API Capabilities for Pipeline Management and Reporting

The public API has been enhanced to support the pipeline actions, in addition to existing task-level actions. These capabilities extend automation possibilities and improve end-to-end lifecycle management through the public API.

Supported Pipeline Management APIs:

• Run pipeline: Trigger Pipeline Execution

• Delete pipeline: Permanently remove a Pipeline.

• View status: Get Pipeline execution status.

• Stop pipeline: Abort an ongoing pipeline run.

• Reset Pipeline: Rerun pipeline for re-execution.

• Update Pipeline: Modify Pipeline execution.

Supported Reporting API:

• Get Pipeline Onboarding Report: Retrieve the total number and the details of the MCP Pipelines onboarded in a specific time range, type or any specific filters.

Streamlined Administration Experience with Redesigned Policy and Admin Screens

The Administration experience has been redesigned to provide clearer navigation, better information hierarchy, and a more consistent visual design across the platform.

Key Highlights:

• Faster, Clearer Policy Management: The Policy screens have been revamped to make it easier to review, understand, and adjust organizational policies, reducing time spent searching for settings and minimizing configuration errors.​

• More Efficient Admin and Settings Workflows: Admin and Settings configuration views follow a cleaner layout and refined grouping of controls, enabling administrators to complete common tasks, such as role updates, access changes, and platform configuration with fewer clicks.​

Unified Insights

Data Accuracy Enhanced with Repository Filters for DORA and Custom Dashboards

To provide users with more granular control over data analysis, a new Repository Filter has been added to the DORA and Custom Dashboards for Deployment Frequency (DF) and Lead Time for Changes (LTFC) KPIs.

Key Highlights:

• Granular Repository-Level Filtering: A Repository Filter has been added to refine DF and LTFC KPI data based on selected GitHub repositories, providing more focused and actionable insights.

• Optimized Selection Control: Up to five repositories can be selected at once, with a prompt displayed when the selection limit is exceeded to maintain clarity and performance.

Export KPI Tables in CSV Format for Seamless Data Sharing

A CSV Export capability has been introduced across all KPI table views to simplify data extraction, sharing, and offline analysis.

Key Highlights:

• Comprehensive Export Access: Easily export KPI tables across key dashboard such as Code Reliability, Developer Experience (DevEx), Automated Quality Practices (AQP), and DORA.

• Filter-Aware Downloads: Exports automatically honor applied filters, providing context-specific datasets that align with each user’s selected view.

• Improved Collaboration and Insight Sharing: Enables teams to share performance insights effortlessly.

Introducing Feature Delivery Rate KPI

A new Feature Delivery Rate KPI has been introduced under Velocity Metrics tab of the Sprint Velocity KPI to help Product Managers measure the throughput of features delivered within a given timeframe. This KPI provides actionable insights into delivery velocity, enabling more accurate planning and performance tracking.

Key Highlights

• Measure Real Feature Throughput: Gain visibility into the average number of EPICs completed per Sprint or Fix Version, helping teams evaluate delivery velocity and consistency.

• Strengthen Planning Accuracy: Analyze only completed EPICs within the selected timeframe to build data-driven forecasts and improve sprint or release planning confidence.

• Enable Flexible Performance Analysis: Switch between Sprint-based or Fix Version–based views to align insights with team workflows and track feature delivery trends over time.

Dynamic GitHub Copilot Executive Summary Report Generation

To enable the delivery of more accurate and context-rich performance insights, the GitHub Copilot Executive Summary Report has been enhanced to support dynamic, filter-aware content generation. With this update, reports are automatically adapted to the selected dashboard parameters, ensuring that stakeholders receive relevant and precise summaries without manual intervention.

Key Highlights:

• Context-Aware Report Generation: Summaries and insights are automatically adjusted based on applied filters such as GitHub Org, Teams, and Workday attributes, ensuring that data remains relevant to the selected context.

• Time-Saving and Consistent Reporting: Reports are generated in a brand-aligned and standardized format, minimizing the need for manual edits and ensuring a consistent presentation of dynamic metrics.

• Leadership-Focused Insights: Context-specific executive summaries are produced to support informed, data-driven decisions for Product Managers and Engineering Leaders.

AI-Driven Insights Reasoning Metric Views for End-to-End Engineering Analytics

Users can view how AI tools like Amazon Q, AI Code Comparison, and Windsurf are impacting their engineering work, all in one analysis. The AI analysis connects pipeline performance, Copilot usage, and sprint velocity so teams can quickly understand what is working and make better decisions.​

Key Capabilities:

• Sprint Velocity Analytics: Sprint velocity trends are queryable and visualized in correlation with AI reasoning outputs from integrated tools.

• Cross-Tool Intelligence: Pipeline performance, Copilot adoption, and code quality metrics are linked to Amazon Q recommendations and Windsurf optimizations.

• Interactive Dashboards: Trend visualizations, contextual reasoning summaries, and performance insights are available for engineering metrics analysis.