The Related Incidents feature provides responders with up to 20 recent incidents on other services that are potentially related to the incident they are investigating. This feature uses a completely online and real-time machine learning algorithm and service dependency data to provide an at-a-glance view of the full scope of incident impact. Related Incidents enriches incidents with deep contextual insights to help responders coordinate an effective team response and mitigate business disruption.

View Related Incidents

You can view the Related Incidents feature in multiple places:

• Web app
• Mobile app
• REST API

Web App

1. From the Incidents page or on a service's detail page, select your desired incident's Title.
2. On the incident's detail page, select the Related Incidents tab.

The Related Incidents tab includes the following information:

• Related Incidents - #: A total count of related incidents.
• Impact Summary: A list of impacted responders, business services, and technical services.

• List of Related Incidents on a Timeline: Related Incidents on the timeline include the following:
  • Incident Details: Click on an incident's title, number of alerts, Assigned To user, or Service to view more details.
  • Dependency Pane: If incidents are related due to a Technical or Business Service dependency relationship, that dependency will be shown here. If there is no service dependency relationship for that incident, click Add One to define your service dependencies. Related Incidents with no dependencies are those that were correlated via machine learning. Read the section on types of Related Incidents to learn more.
  • Information Icon: View why the incident was suggested as related by clicking the icon in the top right corner of the Related Incidents card.
  • Thumbs Up/Thumbs Down: Indicate whether you agree that an incident is or is not related to the one you are investigating, and give the machine learning model feedback. Feedback is real-time, completely processed online, and is applied to suggest future Related Incidents. Click the numbers below the thumbs up/down to view who voted for each option.

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Thumbs Up/Down Visibility

The thumbs up/thumbs down component is only visible on incidents generated by the machine learning algorithm. It takes about 5–10 human inputs to train the algorithm on a new behavior.

Mobile App

1. On the Open Incidents screen, select an incident to navigate to its details page.
2. Tap Overview, scroll down to the Triage section, and tap Related Incidents.

REST API

To access Related Incidents via the REST API, visit the API documentation.

Related Incident Types

There are two types of Related Incidents:

• Machine-Learning-Based Related Incidents
• Dependency-Aware Related Incidents

Machine-Learning-Based Related Incidents

Machine-Learning-Based Related Incidents are determined by three main factors:

• How close in time the incidents were created.
• How related the alert metadata is.
• Human response behavior: thumbs up/thumbs down and/or merging incidents across services.

You can identify Machine-Learning-Based Related Incidents by clicking the icon or by the presence of the thumbs up/thumbs down feedback mechanism.

Dependency-Aware Related Incidents

Some Related Incidents may be determined by your technical or business service dependencies. When two incidents are triggered within five minutes of each other and one service is directly dependent on the other, these incidents will be marked as related. Similarly, if incidents occur within this time frame and share a parent business service, they will also be marked as related. These service relationships will be identified in the Dependency pane on individual Related Incident cards.

FAQ

Related Incidents vs. Past Incidents: What's the difference?

Past Incidents is a tab that displays resolved incidents from the past that were on the same service and may be similar to a current, open incident. Related Incidents, on the other hand, uses machine learning to determine what other active incidents — with a status of triggered or acknowledged — across all services are related to a current incident.

In other words, Past Incidents answers the question "Have similar incidents happened on this service in the past?" while Related Incidents answers "What is going on right now that I should be aware of?"

Why don't I see any related incidents?

Here are some common reasons:

• The incident seems unique: The algorithm was unable to find another recent incident with metadata related to this one. If it identifies any while this incident is still open, they will appear in the Related Incidents tab.
• There is not enough data yet: Your account does not have enough data yet to start finding relationships among incidents. Continue to use PagerDuty normally and Related Incidents will begin to appear.

Why do my Related Incidents appear to have the same title?

The incident title is often determined by the title of the initial alert that triggers an incident. It is likely that the machine learning algorithm determined that alerts created close in time and with the same title are related.

Why am I seeing resolved related incidents?

While Related Incidents is designed to surface ongoing incidents from other services, on occasion responders may see recently resolved incidents that provide additional context for the incident they are currently working on.

Why don't I see Related Incidents from the same service?

This is by design. If you notice a high volume of related or duplicate incidents on a particular service, we recommend enabling Intelligent Alert Grouping to reduce noise on that service and prevent duplicate incidents from being created.

How do I configure Related Incidents?

This feature does not require any setup to begin working with it. Through continued use and user feedback, you can expect its results to become more relevant over time.

You must have access to the PagerDuty AIOps add-on product to access this feature.

How do I improve future Related Incidents results?

The machine learning algorithm can be influenced by user feedback. It looks at human response behavior including thumbs up/down feedback and cross-service merges to improve future Related Incidents.