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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.

The Latest

Two years ago, almost every customer conversation about AI started with the same questions: Which model should we use? What can it do? Is it ready for the enterprise? Today, those discussions have moved on. CIOs are far more interested in how to govern AI, integrate it with existing systems, prepare their workforce and make it part of everyday operations. The challenge is no longer to prove that AI can deliver value. It's instead about how to embed AI into the business in a way that's secure, scalable and delivers measurable outcomes ...

 

Two things happened to production incidents between 2023 and now, and they did not happen at the same speed. The first is that a class of dependency that barely existed three years ago now accounts for one incident in ten. Incidents disclosed by AI model and AI application providers rose from 1.7% of all disclosed unplanned incidents in 2023 to 10.7% in 2026 year to date, roughly a sixfold rise; that counts only incidents at AI companies themselves, so the true share is higher. The second is that the time to close an incident has not come down ...

When an AI assistant gives an incomplete or incorrect answer, teams often blame the model. They adjust prompts, switch models, increase context windows or test a new retrieval strategy. However the model may not be a problem. In many enterprise AI workflows, the problem begins inside the document-ingestion pipeline ...

If you talk to any security or observability teams right now, they're all fighting the same fire: their tooling was built to ingest X, but their sources are pumping Y and soon to be doing Z. The knee-jerk reaction is always the same: we need more platform. However, this reaction is wrong. Let me explain why, because the solution to this problem is foundational, not financial. Instead of hurling yet more money at the problem, make sure you've done what's needed upstream ...

Rapid AI adoption and the unique ways AI workloads operate is redefining the scope and structure of what these teams must deliver. This shift is forcing organizations to rethink how they manage scale, automation, and control, according to The State of SRE and Platform Engineering 2026, a new report from Dynatrace ...

AI is usually talked about as a software tool, but it also depends heavily on the network behind it. Whether a company is using AI for chatbots, automation, monitoring, analytics, or employee support, all of that information has to move across the network in a reliable and secure way. That means AI is not just an application decision. It is also an infrastructure decision. Before organizations rush into AI, they should ask a simple question: Is our network ready to support it? ...

Enterprise AI often lacks governed access to where business processes actually execute. Without that access, AI agents may be able to reason, but they cannot operate reliably across enterprise workflows. For AI agents to effectively carry out workflows, they will require integration-layer context and controls. Organizations can implement these prerequisites by providing AI with managed access to the middleware layer ...

Enterprise networks rarely behave the same way for very long. A routing adjustment in one region may unexpectedly alter application performance in another. A cloud migration may introduce hidden dependencies that go unnoticed until an outage occurs. All the while, the network is managed by several different teams, each of whom use different tool sets — and as a result, have different views of the network ... There’s usually an engineer who remembers why traffic fails over a certain way between sites, or which transparent firewall was added where. The problem is that human memory cannot scale alongside enterprise-scale networks ...

Ask an infrastructure team how confident they are in their ability to govern AI, and most will tell you they've got it handled. A recent survey of 406 IT decision-makers and platform engineering leaders found 86% expressing exactly that confidence. Ask the same group whether they have a formal written AI governance policy, and the number drops to 30%, according to Spacelift's Infrastructure Automation Report ...

In MEAN TIME TO INSIGHT Episode 27, Shamus McGillicuddy, EMA VP of Research, Network Infrastructure and Operations, and Parker Hathcock, EMA Research Director covering IT Service/Operations (ServiceOps), discuss observability unification in modern IT operations ... 

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.

The Latest

Two years ago, almost every customer conversation about AI started with the same questions: Which model should we use? What can it do? Is it ready for the enterprise? Today, those discussions have moved on. CIOs are far more interested in how to govern AI, integrate it with existing systems, prepare their workforce and make it part of everyday operations. The challenge is no longer to prove that AI can deliver value. It's instead about how to embed AI into the business in a way that's secure, scalable and delivers measurable outcomes ...

 

Two things happened to production incidents between 2023 and now, and they did not happen at the same speed. The first is that a class of dependency that barely existed three years ago now accounts for one incident in ten. Incidents disclosed by AI model and AI application providers rose from 1.7% of all disclosed unplanned incidents in 2023 to 10.7% in 2026 year to date, roughly a sixfold rise; that counts only incidents at AI companies themselves, so the true share is higher. The second is that the time to close an incident has not come down ...

When an AI assistant gives an incomplete or incorrect answer, teams often blame the model. They adjust prompts, switch models, increase context windows or test a new retrieval strategy. However the model may not be a problem. In many enterprise AI workflows, the problem begins inside the document-ingestion pipeline ...

If you talk to any security or observability teams right now, they're all fighting the same fire: their tooling was built to ingest X, but their sources are pumping Y and soon to be doing Z. The knee-jerk reaction is always the same: we need more platform. However, this reaction is wrong. Let me explain why, because the solution to this problem is foundational, not financial. Instead of hurling yet more money at the problem, make sure you've done what's needed upstream ...

Rapid AI adoption and the unique ways AI workloads operate is redefining the scope and structure of what these teams must deliver. This shift is forcing organizations to rethink how they manage scale, automation, and control, according to The State of SRE and Platform Engineering 2026, a new report from Dynatrace ...

AI is usually talked about as a software tool, but it also depends heavily on the network behind it. Whether a company is using AI for chatbots, automation, monitoring, analytics, or employee support, all of that information has to move across the network in a reliable and secure way. That means AI is not just an application decision. It is also an infrastructure decision. Before organizations rush into AI, they should ask a simple question: Is our network ready to support it? ...

Enterprise AI often lacks governed access to where business processes actually execute. Without that access, AI agents may be able to reason, but they cannot operate reliably across enterprise workflows. For AI agents to effectively carry out workflows, they will require integration-layer context and controls. Organizations can implement these prerequisites by providing AI with managed access to the middleware layer ...

Enterprise networks rarely behave the same way for very long. A routing adjustment in one region may unexpectedly alter application performance in another. A cloud migration may introduce hidden dependencies that go unnoticed until an outage occurs. All the while, the network is managed by several different teams, each of whom use different tool sets — and as a result, have different views of the network ... There’s usually an engineer who remembers why traffic fails over a certain way between sites, or which transparent firewall was added where. The problem is that human memory cannot scale alongside enterprise-scale networks ...

Ask an infrastructure team how confident they are in their ability to govern AI, and most will tell you they've got it handled. A recent survey of 406 IT decision-makers and platform engineering leaders found 86% expressing exactly that confidence. Ask the same group whether they have a formal written AI governance policy, and the number drops to 30%, according to Spacelift's Infrastructure Automation Report ...

In MEAN TIME TO INSIGHT Episode 27, Shamus McGillicuddy, EMA VP of Research, Network Infrastructure and Operations, and Parker Hathcock, EMA Research Director covering IT Service/Operations (ServiceOps), discuss observability unification in modern IT operations ...