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BigPanda Accelerates AIOps Adoption With New Integrations and Self-Service APIs

New Capabilities Foster Deeper Collaboration Between Agile and Traditional IT Ops Teams by Detecting and Responding to Alerts Faster and Automating Incident Management Workflows

BigPanda announced a suite of new and updated integrations and self-service APIs designed to accelerate AIOps adoption across fragmented teams and tools.

Specifically, native integrations with Jira and AppDynamics deliver a new framework for collaboration and communication, while a trio of self-service APIs enables organizations to scale AIOps deployments with ease.

According to Gartner, “by 2023, 80% of ITSM teams that have not adopted an agile approach will be made redundant by DevOps practices and approaches. As organizations increase their reliance on DevOps toolchains such as continuous integration/continuous delivery (CI/CD), solutions demand automated integration with incident management processes.” BigPanda’s new capabilities address this challenge by making it easier to foster deeper collaboration between agile and traditional ops teams by detecting and responding to alerts faster across the IT landscape by automating incident management workflows.

“Automating repeated tasks and workflows is key to accelerating incident response and allowing IT Ops to keep up with the pace of change and innovation that DevOps and SRE teams need to thrive,” said Elik Eizenberg, co-founder and CTO at BigPanda. “Our latest innovations give our customers the ability to embrace the rapid innovation of products and services while adhering to the same level of quality, reliability and serviceability that their end customers have come to expect.”

New Integrations to Unify DevOps and IT Ops Teams and Tools

BigPanda’s new native and improved integrations include:

Jira Cloud: An all-new Jira integration allows teams to automatically create Jira Issues based on correlated BigPanda incidents and keep them in sync bi-directionally. This allows both traditional IT Ops and NOC teams, as well as agile DevOps and SRE teams, to collaborate and remain synced while still following their existing workflows and processes.

Similarly, an upcoming Jira Changes integration automatically notifies BigPanda of new or updated changes in the Jira Change Management module. This makes it easier for BigPanda to ingest these changes and match them against associated BigPanda incidents as part of BigPanda’s Root Cause Changes capability.

AppDynamics: AppDynamics alerts and their payload data play an important role for IT Ops, NOC, DevOps and SRE teams in detecting, investigating and responding to incidents. This new and enhanced AppDynamics integration provides out-of-the-box capabilities to ingest AppDynamics Health Rule and Error events, normalizing them into BigPanda alerts with minimal end-user configuration. This improves visibility across the organization with shared situational awareness for all operations teams.

Self-Service APIs for Improved AIOps Scalability

BigPanda’s new capabilities also include three self-service APIs:

Environments API: BigPanda Environments are fully customizable and filtered incident views that simplify incident management. Environments can be defined by any parameter, such as role and responsibility, location, severity, applications and more. Once created, incidents can be automatically routed to those environments for user action or trigger workflow automations such as auto-sharing or auto-escalations.

The new Environments API enables administrators to reduce complex manual configuration by templatizing and automating the creation and configuration of environments at scale. For complex, fast-moving enterprises, this makes incident response much faster and easier.

Correlation Patterns API: BigPanda’s Open Box Machine Learning technology automatically generates correlation patterns based on four dimensions: time, topology, context, and alert type. Administrators can choose to activate these patterns, reject them, or further customize them, all without the need for data scientists or other experts. This capability was previously only available via the pattern editor UI.

The new Correlation Patterns API gives users the ability to take action on automatically generated patterns, significantly speeding up the process of onboarding new applications or services and giving users the ability to create new correlation patterns when needed.

Incidents API: Incidents are at the core of BigPanda’s functionality, and the new Incidents API allows teams in complex enterprises to move faster and scale more seamlessly with the ability to manage BigPanda incidents via two main capabilities:

- Incident Search: The ability to filter all incidents in a BigPanda environment and return those that meet specific conditions. Users can set sort order and pagination rules and can query incidents by tag, time frame, source system, and more.

- Incident Actions: Facilitate a range of incident management actions directly through the API to merge, assign, snooze, tag, and comment on incidents. Future milestones will allow additional actions such as split, share, and resolve.

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BigPanda Accelerates AIOps Adoption With New Integrations and Self-Service APIs

New Capabilities Foster Deeper Collaboration Between Agile and Traditional IT Ops Teams by Detecting and Responding to Alerts Faster and Automating Incident Management Workflows

BigPanda announced a suite of new and updated integrations and self-service APIs designed to accelerate AIOps adoption across fragmented teams and tools.

Specifically, native integrations with Jira and AppDynamics deliver a new framework for collaboration and communication, while a trio of self-service APIs enables organizations to scale AIOps deployments with ease.

According to Gartner, “by 2023, 80% of ITSM teams that have not adopted an agile approach will be made redundant by DevOps practices and approaches. As organizations increase their reliance on DevOps toolchains such as continuous integration/continuous delivery (CI/CD), solutions demand automated integration with incident management processes.” BigPanda’s new capabilities address this challenge by making it easier to foster deeper collaboration between agile and traditional ops teams by detecting and responding to alerts faster across the IT landscape by automating incident management workflows.

“Automating repeated tasks and workflows is key to accelerating incident response and allowing IT Ops to keep up with the pace of change and innovation that DevOps and SRE teams need to thrive,” said Elik Eizenberg, co-founder and CTO at BigPanda. “Our latest innovations give our customers the ability to embrace the rapid innovation of products and services while adhering to the same level of quality, reliability and serviceability that their end customers have come to expect.”

New Integrations to Unify DevOps and IT Ops Teams and Tools

BigPanda’s new native and improved integrations include:

Jira Cloud: An all-new Jira integration allows teams to automatically create Jira Issues based on correlated BigPanda incidents and keep them in sync bi-directionally. This allows both traditional IT Ops and NOC teams, as well as agile DevOps and SRE teams, to collaborate and remain synced while still following their existing workflows and processes.

Similarly, an upcoming Jira Changes integration automatically notifies BigPanda of new or updated changes in the Jira Change Management module. This makes it easier for BigPanda to ingest these changes and match them against associated BigPanda incidents as part of BigPanda’s Root Cause Changes capability.

AppDynamics: AppDynamics alerts and their payload data play an important role for IT Ops, NOC, DevOps and SRE teams in detecting, investigating and responding to incidents. This new and enhanced AppDynamics integration provides out-of-the-box capabilities to ingest AppDynamics Health Rule and Error events, normalizing them into BigPanda alerts with minimal end-user configuration. This improves visibility across the organization with shared situational awareness for all operations teams.

Self-Service APIs for Improved AIOps Scalability

BigPanda’s new capabilities also include three self-service APIs:

Environments API: BigPanda Environments are fully customizable and filtered incident views that simplify incident management. Environments can be defined by any parameter, such as role and responsibility, location, severity, applications and more. Once created, incidents can be automatically routed to those environments for user action or trigger workflow automations such as auto-sharing or auto-escalations.

The new Environments API enables administrators to reduce complex manual configuration by templatizing and automating the creation and configuration of environments at scale. For complex, fast-moving enterprises, this makes incident response much faster and easier.

Correlation Patterns API: BigPanda’s Open Box Machine Learning technology automatically generates correlation patterns based on four dimensions: time, topology, context, and alert type. Administrators can choose to activate these patterns, reject them, or further customize them, all without the need for data scientists or other experts. This capability was previously only available via the pattern editor UI.

The new Correlation Patterns API gives users the ability to take action on automatically generated patterns, significantly speeding up the process of onboarding new applications or services and giving users the ability to create new correlation patterns when needed.

Incidents API: Incidents are at the core of BigPanda’s functionality, and the new Incidents API allows teams in complex enterprises to move faster and scale more seamlessly with the ability to manage BigPanda incidents via two main capabilities:

- Incident Search: The ability to filter all incidents in a BigPanda environment and return those that meet specific conditions. Users can set sort order and pagination rules and can query incidents by tag, time frame, source system, and more.

- Incident Actions: Facilitate a range of incident management actions directly through the API to merge, assign, snooze, tag, and comment on incidents. Future milestones will allow additional actions such as split, share, and resolve.

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Over the last year, we've seen enterprises stop treating AI as “special projects.” It is no longer confined to pilots or side experiments. AI is now embedded in production, shaping decisions, powering new business models, and changing how employees and customers experience work every day. So, the debate of "should we adopt AI" is settled. The real question is how quickly and how deeply it can be applied ...

In MEAN TIME TO INSIGHT Episode 20, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA presents his 2026 NetOps predictions ... 

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My latest title for O'Reilly, The Rise of Logical Data Management, was an eye-opener for me. I'd never heard of "logical data management," even though it's been around for several years, but it makes some extraordinary promises, like the ability to manage data without having to first move it into a consolidated repository, which changes everything. Now, with the demands of AI and other modern use cases, logical data management is on the rise, so it's "new" to many. Here, I'd like to introduce you to it and explain how it works ...

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