Eng Intelligence provides you with key metrics and high-level data to gain insights into services, bottlenecks in the pull request lifecycle, incident response, and more. These metrics can give context to cross-team activities and indicate areas that need deeper investigation, allowing you to quickly remediate and improve productivity across your teams.

Eng intelligence features include:

• Dashboards:

  • The DORA Dashboard gives clear insights into how fast, reliable, and efficient your development practices are.

  • The visualizes your team's success across the software development lifecycle.

  • The provides insight into Copilot adoption and engagement across your engineering teams.

  • allow you to create shared views of the key engineering metrics that matter most to your organization

• : Analyze trends over time and drill into the underlying data for investigation. Explore metrics for , , project management (), and incident management ().

• : The ability to define your own custom time series metrics to power the analytics in Eng Intelligence, drawing from your integrations with Cortex or your organization's internal data. Currently custom metrics are only available in the All Metrics (Classic View) but planned to be added to the new experiences.

Learn about .

Eng Intelligence overview video

Measuring success with Eng Intelligence in Cortex

In practice, success means that teams are not only tracking metrics like cycle time, deployment frequency, change failure rate, and mean time to recovery (MTTR), but they are also taking action to make progress based on how the metrics are trending. The focus should be less about hitting specific benchmarks and more about creating continuous feedback loops that drive consistent improvement.

Eng Intelligence goals

Eng Intelligence metrics provide clear, actionable insights into how your teams work, allowing you to find opportunities to improve delivery speed, reliability, and quality.

Before setting goals, we recommend establishing a baseline on your top-priority metrics.

Early signs of improvement

Leading indicators show that teams are learning from Eng Intelligence metrics and effectively making changes in their day-to-day work. For example:

• Faster feedback loops: Time to first PR review, Time to PR approval, and Work item lead time steadily decrease over a given period.

• Smaller, more frequent PRs: PR size trends downward while merged PR count increases.

• Balanced workloads: More unique PR authors over time, showing distributed contribution. For project management metrics, a balance of Work items created and Work items completed shows healthy workload management.

Outcomes of improvement

Lagging indicators can measure whether your organization is seeing tangible improvements in delivery and reliability. For example:

• Cycle time improves from baseline.

• Deployment frequency increases without increased failure rates.

• MTTR decreases after incidents.

• Higher engineering satisfaction is reported in internal surveys as bottlenecks are resolved.

Best practices for reviewing Eng Intelligence metric trends

To make your data more meaningful, we recommend the following best practices:

• Segment by teams and services to avoid skewed organization-wide averages.

• Look for trends rather than snapshots. Success is measured over time; a short-term fluctuation can be misleading.

• When you identify process gaps with Eng Intelligence, take action to drive adoption of standards: Use or to encourage your teams to get their owned services aligned with standards. Use to streamline repeatable tasks for engineers so they can focus on other work.

Defining success

The following are common ways to confirm successful use of Eng Intelligence features:

• Leadership uses the dashboards to make strategic decisions (e.g., during planning cycles).

• Teams are actively using the dashboards to spot bottlenecks.

• Metrics drive measurable changes in process, tooling, and culture.

• Improvements are sustained and repeatable, not just short-term spikes.

Accessing Eng Intelligence

To get started, click Eng Intelligence from the main nav in Cortex.

See the docs for more information on each part of Eng Intelligence:

The DORA framework (DevOps Research and Assessment) is a set of metrics that help teams measure software delivery performance. Integrated into your internal developer portal, DORA metrics give engineering teams clear insights into how fast, reliable, and efficient their development practices are. This empowers teams to track progress, identify bottlenecks, and drive continuous improvement — all within the same place they manage services and deploy code. Read more about these metrics in the dora.dev guide.

Use the DORA Dashboard in Cortex to evaluate the speed and stability of your software delivery process. Visualize your team’s success across the software process and get a clear view of cycle time, deployment frequency, change failure rate, and time to resolution.

Using the DORA Dashboard

To view the dashboard, click Eng Intelligence > DORA Dashboard in the main nav.

Adjust the time range

By default, the dashboard displays data from the last last month. To change the date range, click Last month and select a new date range.

Apply a filter option

For each chart, you can apply filters for group, owner, repository, user label, and more. To select filters, click Filter in the upper right corner of a graph.

Metric visualizations

You can select a different operation for the Cycle time and Time to resolution graphs. Options include average, sum, max, min, P95, and median. Both graphs display the average by default.

Cycle time

Cycle time represents the time it takes for a single PR to go through the entire coding process. Shorter cycle times indicate a more agile team that is able to quickly respond to changing needs.

Calculation: The time between the first commit on a PR to when the PR is merged.

Best practice: Aim for lower cycle times to ensure a faster feedback loop and reduced context switching. Rather than benchmarking the overall cycle time, set benchmarks for the individual parts of the cycle (time to open, time to approve, time to first review, time to merge).

Note: This metric is not supported for or .

Deployment frequency

Deployment frequency measures how often your team successfully releases code to production, serving as a key indicator of your delivery velocity and operational maturity. This DORA metric reflects your team's ability to deliver value continuously and respond quickly to market demands.

Calculation: The number of deployments over a given period of time.

Best practice: Depending on your organization, a successful benchmark could be multiple deployments per day or per week.

Change failure rate

Change failure rate measures the percentage of deployments that result in production failures, serving as a critical indicator of deployment stability and code quality. This DORA metric reveals how often your releases introduce bugs, outages, or performance issues that impact users.

Calculation: Number of rollbacks / number of deployments created.

Best practice: Aim for a failure rate less than 15%.

Time to resolution (MTTR)

Time to resolution measures how quickly your team recovers from production failures, reflecting your incident response capabilities and system resilience. Also known as mean time to recovery (MTTR), this metric indicates how well-prepared your team is to handle inevitable production issues.

Calculation: Incident resolution time - incident opened time.

Best practice: This benchmark may differ depending on how critical a system is. For less critical systems, aim for a measure of less than 1 day. For critical systems, aim for under 1 hour.

Eng Intelligence Dashboards provide engineering leaders and teams with real-time visualizations into key metrics, velocity and productivity trends, incident and quality insights, and historical organzation-wide rollups. Dashboards are natively integrated with your Cortex catalog, Scorecards, and Initiatives, allowing you to move from insight to action.

See the documentation linked below to learn more about each dashboard:

• DORA Dashboard: Visualize the four key DORA metrics: Cycle time, deployment frequency, change failure rate, and MTTR.

• Velocity Dashboard: Monitor version control metrics such as PR cycle time, PR size, and PR success rate, giving insight into your process efficiency and areas to target for improvement.

• : Build tailored dashboards by combining metric visualizations into a unified, shareable view.

Custom Dashboards make it easy to create shared views of the key engineering metrics that matter most to your organization. They allow you to:

• Aggregate and display multiple metrics: Combine different saved report modules into a single dashboard.

• Personalize views: Create dashboards tailored to specific teams or use cases, enabling focused monitoring.

  • For example, you might want to track specific incident response metrics or you oculd create a comprehensive view of delivery by combining metrics across deployment, version control, and project management systems.

• Share insights: Share dashboards within your organization, ensuring your stakeholders have access to relevant information without manual reporting.

• Drive action: By surfacing the trends that matter to you, dashboards help you identify bottlenecks, track progress toward goals, and make data-driven decisions.

Create a Custom Dashboard

Prerequisites

Before getting started:

• Make sure you have configured metric modules and saved them in Metrics Explorer.

• You must have the Configure Eng Intelligence permission.

Add modules to the dashboard

1. Navigate to Eng Intelligence > Dashboards. In the upper right corner, click Create Dashboard.

2. Enter a name and description and choose an icon, then click Create Dashboard.

3. Select an existing metric module to add to your dashboard.

   • To create a new module, click

Managing Custom Dashboards

Share a Custom Dashboard

Dashboards are automatically visible within your organization to users with the View Eng Intelligence permission, so everyone can view the same insights and stay aligned.

To create a share link to send to another user, click the 3 dots icon in the upper right, then click Share.

Edit a Custom Dashboard

While viewing the dashboard, click Edit in the upper right corner. Go through the steps of , then click Save.

Custom Dashboard examples

See examples of Custom Dashboards below:

The Velocity Dashboard provides a one-stop-shop into your software development lifecycle helping you and your teams understand any bottlenecks in your process. Get a clear view of PR cycle time to identify bottlenecks and spot inefficiencies, compare pull request size against commit activity, and visualize your team’s success rate across the entire review process — all in one place.

Using the Velocity Dashboard

To view the dashboard, click Eng Intelligence > Velocity Dashboard in the main nav.

The AI Impact Dashboard provides insight into Copilot adoption and engagement across your engineering teams, including visualizations for:

• Impact by team AI adoption rate

  • Correlate team adoption rate to delivery and reliability metrics. Adoption rates are provided daily by comparing active users against Copilot seats.

• Impact of Copilot users vs. non-Copilot users

  • Compare delivery and reliability side-by-side between users who leveraged AI tools within the last 7 days, and those who did not. Understand whether recent AI usage affects engineering performance.

• AI adoption trends

  • View the overall trends for AI adoption across your organization.

Use these insights to identify issues and .

Using the AI Impact Dashboard

Prerequisites

Before getting started:

• You must have configured. See instructions below for each integration method:

  • Cortex GitHub app

    • If configured before October 14, 2025, you must to accept two new permissions.

  • Custom GitHub App

View the Dashboard

Navigate to to see the full list of Cortex-built and available in your workspace. Click the Copilot Dashboard.

This Dashboard contains multiple charts that you can filter and compare with key engineering metrics:

Filter and configure the Dashboard

Overlay a data point

To overlay a data point, click the dropdown in the upper left corner of a chart:

Filter the chart

Apply filters to further configure your dashboard by:

• Time range: In the upper right corner of the page, click the time range filter to apply a different time range. By default, the dashboard shows data from the last 30 days.

• Display: In the upper right corner of the page, click Display and choose whether to view the graphs by day, week, or month.

• Operation: In the upper right corner of a graph, click Average to open the dropdown menu for operations. You can choose from average, max, median, min, P95, and sum.

Driving continuous improvement

Use the AI Impact Dashboard to track and correlate key AI adoption metrics with engineering performance metrics. These insights allow you to identify issues in your processes, which enables the ability to take action and drive improvements.

Example scenario: You view your dashboard and notice that teams who have higher rates of AI adoption have a lower average cycle time (the time it takes for a single PR to go through the entire coding process). However, you also see a spike in incident frequency for those teams.

• Identify the issue: You conclude that their process is more efficient, but as a tradeoff, they're shipping code that causes more incidents. You brainstorm with the affected engineering teams to learn how their processes have changed since adopting AI. You learn that SonarQube code coverage is lower than it has been previously.

• Take action: You already have an launched, and you see that the rule "Test coverage minimum met" is failing. You can create an on that Scorecard to ask developers to meet a particular rule by a specified deadline. In this case, you create an Initiative that asks the developers to pass the failing rule (i.e., they need to ensure higher than 80% test coverage) by the end of the month.

  • Initiatives and Scorecard tasks appear as to-do items on a developer's in Cortex.

Define your own custom time series metrics to power the analytics in your Eng Intelligence dashboard, drawing from your integrations with Cortex or your organization's internal data. In addition to seeing these in Eng Intelligence, you’ll also be able to view these in the entity pages and use them in Scorecards.

After defining a custom metric, the metric data can be provided via the following methods:

• API: Post custom metric data to Cortex via .

• CQL: Compute data based on a CQL query that is evaluated by Cortex every 12 hours.

In the All Metrics page, view metrics pulled in from the Cortex deploys API, version control integrations (Azure DevOps, Bitbucket, GitHub, and GitLab), Jira, and PagerDuty.

Accessing All Metrics

To view, click Eng Intelligence > All Metrics in the main nav:

Using All Metrics

The All Metrics view aggregates data from your connected entities to calculate critical metrics based on your organization's priorities. The data is presented by team, group, or individual, and can be filtered by time range. Cortex provides a set of , but you can also to track here.

These values are recalculated every hour. For count metrics (e.g., PRs opened) , 0 is displayed if no data is available. For average metrics (e.g., average PR open to close time), N/A is displayed if no data is available to calculate averages.

Apply time range and team filters

By default, All Metrics displays data from the last 7 days.

To filter by time range: In the upper right corner of Eng Intelligence, click Last 7 days, then select a new time range for your metrics display:

To filter by team, group, or owner:

1. Click Filter in the upper right corner.

2. Click into Group, Owner, or Team, and select filter options.

3. Click Apply.

Group by team hierarchy

By default, each Team entity in Cortex is displayed in its own dedicated row. To group by the you've created, click View as hierarchy.

Group by entity type

By default, All Metrics displays Team data. In the upper left corner, click the Team dropdown to select a different entity type:

Group by user label

After you have , you can group by labels.

Click the Group by dropdown and select a label you want to group by. The grouping will be added as a row to the metrics table, along with separate rows for each member of the grouping.

View more details for an entity

To better understand the data behind a trend you see, click an entity to open a side panel with more information:

• Under the Related activity tab, see available metrics and recent activity.

• Under the Trends tab, see a historical performance graph for each metric.

In the upper right corner of the panel, you can adjust the time range for the graphs to be anywhere between the last 7 days and 6 months. This will update the graph view and maps to the table, so all metrics will reflect the new timeframe.

Show Scorecard view

In the upper right corner, click Display. In this drop-down, you can choose whether to display entities in their associated hierarchies and you can select a Scorecard.

When you select a Scorecard, Scorecard performance is overlayed in Eng Intelligence when grouped by team or service. This view is not available when grouping by group, user, or owner. The icon representing the Scorecard level achieved by each entity will appear next to the entity name:

Metrics

Users with the Configure custom metrics permission can create for All Metrics, or you can use the built-in metrics listed below.

These metrics are pulled from the , version control integrations (Azure DevOps, Bitbucket, GitHub, and GitLab), Jira, and PagerDuty.

Deploy metrics

Version control metrics

Jira metrics

PagerDuty metrics

All Metrics settings

Change All Metrics appearance

From the , users with the Configure Eng Intelligence permission can also choose which columns to display and adjust the order of columns in the All Metrics view.

Set filtering for metric calculation

Under Settings > Eng Intelligence, in , users with the Configure Eng Intelligence permission can set filters for some pre-defined metrics:

• Under Deploys, select the deploy environments you want to include in the calculation of deploy frequency and deploy failure rate. - If none are selected, all deploys will be included.

• Under Pull requests, select the authors you want to exclude from the calculation of PR-related metrics.

  • If none are selected, PRs from all authors will be included.

Create and manage user labels for grouping

User labels in Eng Intelligence allow you to group users into cohorts to analyze metrics based on different factors. This can be useful for benchmarking one engineer’s metrics against the average within a cohort, comparing metrics between engineers who use different tools to complete their work, and understanding metrics by different variables: location (e.g., in office or remote), engineer level (staff vs. lead engineer), tech stack (frontend vs. backend), and more.

Users who have the Configure user labels permission can create and apply labels.

The instructions below describe how to use this feature in the Cortex UI. See the Cortex API documentation for instructions on creating and managing user labels programmatically.

Create a user label

1. In Cortex, navigate to the .

   1. Click your avatar in the lower left then click Settings.

   2. Under Eng Intelligence, click User labeling.

2. In the upper right corner, click Create label

After saving, the label will appear under the Label management tab in the Eng Intelligence settings page.

View applied user labels

In the under the User labeling tab, you can view a list of users and their applied labels. Note that these labels are only displayed in Eng Intelligence, and not in other pages within Cortex.

Assign a user label to a user

1. In Cortex, navigate to the .

2. In the list, locate the user you want to add a label to. Under the "Labels" column for that user, click the pencil icon.\
3. In the side panel, click into the dropdown to select a pre-existing label. To create a new label, type in a name then click +Create in the dropdown.

Assign user labels in bulk

1. In Cortex, navigate to the .

2. Check the boxes next to the users you want to edit. As you check names, a banner will appear at the bottom of the page showing how many users are selected. In that banner, click Edit labels.

3. In the bulk edit modal, enter the labels you want to add to the users, then click Set labels.

After applying labels to users, you can while viewing Eng Intelligence metrics.

Configuring Groupings for Jira Metrics

You can add custom groupings to Jira Issues based on labels, issue types, and components. The number of tickets completed for each grouping will be calculated in Eng Intelligence using the custom name you configure for the grouping.

1. Navigate to the Eng Intelligence settings page and click .

2. On the right side of the page, click Add issue grouping.

3. In the modal, configure the issue grouping:

   • Name: Enter a name for the grouping.

Metrics Explorer enables you to analyze metric trends over time and drill into specific data points for detailed investigation. Use this tool to understand patterns in your development process and identify areas for improvement.

You can save your favorite Metrics Explorer views as report modules, allowing you to revisit key metrics without needing to reapply filters or display settings. Saved report modules make it easy to monitor key metrics, like Cycle Time for a particular team or over a given time period, on a consistent basis, and they can be used to build Custom Dashboards.

Access Metrics Explorer

To view, click Eng Intelligence > Metrics Explorer from the main nav.

View saved report modules

On the left side of Metrics Explorer, see a list of all saved metric modules:

Using Metrics Explorer

Step 1: Configure a report module

1. On the page, click the metric name in the upper left corner. By default, Cycle time is displayed.

   • A modal will appear.

2. On the left side of the modal, select a metric. On the right side, depending on which metric you choose, you can select an operation.

Next, you can optionally segment the metrics and apply filters before .

Step 2: Segment and filter the metrics

You can segment the metrics by person, entity, PR, or owner, and you can filter a graph by time range, teams, author, and repository. You can also sort the columns.

Step 3: Save the report module

Once you've configured a view you'd like to revisit with a specific metric, filters, and time ranges, you can save it as a report module:

• While viewing a module, click Save in the upper right corner of the page. Enter a name and description for the module.

Managing saved report modules

After saving, your report will appear in the module list in Metrics Explorer, where you can:

• Add it to a

• Reopen it at any time without reconfiguring filters

• Rename, update metric/filter settings, and re-save as needed

• Create a duplicate of the module: "Save As" a new module to create a copy of the settings as a new starting point

Share a report module

After selecting a data point and applying filters, you can share the browser URL with other people who have access to your Cortex workspace. The URL query parameters include timestamps, so the shared Metrics Explorer page will reflect the same results across different timezones.

Metrics available in the Metrics Explorer

Expand the tiles below to learn how the metric is calculated and the best practice for measuring success.

AI tools

AI usage metrics are pulled from .

Deployment metrics

Deploy metrics are pulled from the .

Incident metrics

Incident metrics are pulled from .

Project management metrics

Project management metrics are pulled from .

Version control metrics

Version control metrics are pulled from , , , and .

Metrics Explorer reference

Metric filter definitions

The available filters differ based on the category of the metric you're viewing.

Expand the tiles below to learn about each of the filters available per metric category.

Segment definitions