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New Relic and Atlassian Introduce Observability Integration for Incidents Tab in Jira

New Relic announced a new integration with Atlassian.

New Relic is integrated into Atlassian’s new capability to track incidents in Jira Software. The integration empowers engineering teams to bolster their incident management and resolution practices with insights gained from software issues detected in New Relic. This enables organizations to more effectively manage current incidents and prevent future ones, freeing more time for product innovation.

The Incidents tab in Jira sends incidents from New Relic and other tools to Jira, allowing teams to quickly learn about the incident and focus on identifying the affected services, entities, and issues. By surfacing post-incident reviews (PIRs) in Jira, teams can assign and manage preventative work to reduce the frequency and volume of costly incidents. New Relic is the first and only observability provider to offer an integration available in early access to Jira customers, which helped shape Jira's new capability.

“At New Relic, we believe in meeting engineers where they are, enabling them to do their best work with their preferred tools. We are delighted to be the first observability platform to integrate with the incidents tab in Jira,” said New Relic Chief Design and Strategy Officer Peter Pezaris. “With our natively-connected solution, we hope to expand access to observability by bringing the right insights into engineers’ existing workflows, helping them manage their work more effectively with the best-of-breed tools they use every day.”

Key benefits of the integration include:

- Enhance incident detection and visibility: Enable development teams to monitor their code’s performance in production and identify associated issues in Jira Software.

- Avoid screen swivel, speed up resolution: Link and create Jira issues with pre-populated incident details from New Relic to help resolve critical issues faster and reduce downtime.

- Prioritize and streamline workflows: Review incidents by priority, affected service, and status to quickly identify incidents that require a team’s attention.

- Establish proactive practices: Create PIRs that include real-time New Relic data to help teams understand root causes, remediation options, and how to prevent recurring incidents.

“We’re on a mission to bring software development and IT operations teams closer together so they can deliver consistently high performing software to their customers,” said Suzie Prince, Head of Product, DevOps, Atlassian. “The New Relic integration automatically surfaces insights and observability data in the new Incidents tab in Jira to further improve collaboration between teams. This can help teams move quickly on active incidents and take action to prevent recurring incidents.”

The New Relic integration for the Jira Incidents tab is the latest collaboration between New Relic and Atlassian. It builds on the recent New Relic errors inbox integration with Jira for error tracking, the Bitbucket quickstart in New Relic Instant Observability to monitor and optimize deployments, and New Relic alerts integration with Jira to help the right teams get the right information in the software development lifecycle.

The Incidents tab in Jira is currently available in early access.

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New Relic and Atlassian Introduce Observability Integration for Incidents Tab in Jira

New Relic announced a new integration with Atlassian.

New Relic is integrated into Atlassian’s new capability to track incidents in Jira Software. The integration empowers engineering teams to bolster their incident management and resolution practices with insights gained from software issues detected in New Relic. This enables organizations to more effectively manage current incidents and prevent future ones, freeing more time for product innovation.

The Incidents tab in Jira sends incidents from New Relic and other tools to Jira, allowing teams to quickly learn about the incident and focus on identifying the affected services, entities, and issues. By surfacing post-incident reviews (PIRs) in Jira, teams can assign and manage preventative work to reduce the frequency and volume of costly incidents. New Relic is the first and only observability provider to offer an integration available in early access to Jira customers, which helped shape Jira's new capability.

“At New Relic, we believe in meeting engineers where they are, enabling them to do their best work with their preferred tools. We are delighted to be the first observability platform to integrate with the incidents tab in Jira,” said New Relic Chief Design and Strategy Officer Peter Pezaris. “With our natively-connected solution, we hope to expand access to observability by bringing the right insights into engineers’ existing workflows, helping them manage their work more effectively with the best-of-breed tools they use every day.”

Key benefits of the integration include:

- Enhance incident detection and visibility: Enable development teams to monitor their code’s performance in production and identify associated issues in Jira Software.

- Avoid screen swivel, speed up resolution: Link and create Jira issues with pre-populated incident details from New Relic to help resolve critical issues faster and reduce downtime.

- Prioritize and streamline workflows: Review incidents by priority, affected service, and status to quickly identify incidents that require a team’s attention.

- Establish proactive practices: Create PIRs that include real-time New Relic data to help teams understand root causes, remediation options, and how to prevent recurring incidents.

“We’re on a mission to bring software development and IT operations teams closer together so they can deliver consistently high performing software to their customers,” said Suzie Prince, Head of Product, DevOps, Atlassian. “The New Relic integration automatically surfaces insights and observability data in the new Incidents tab in Jira to further improve collaboration between teams. This can help teams move quickly on active incidents and take action to prevent recurring incidents.”

The New Relic integration for the Jira Incidents tab is the latest collaboration between New Relic and Atlassian. It builds on the recent New Relic errors inbox integration with Jira for error tracking, the Bitbucket quickstart in New Relic Instant Observability to monitor and optimize deployments, and New Relic alerts integration with Jira to help the right teams get the right information in the software development lifecycle.

The Incidents tab in Jira is currently available in early access.

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AI is becoming the operating system of the enterprise. It acts as an invisible coordination layer that understands intent, connects systems, and executes work across complex SaaS environments. Previously, employees had to click through multiple systems — CRM, ERP, support tools, collaboration platforms — to complete a single task. Now, instead of navigating each application manually, they can simply state what they need to accomplish ...

In 2026, the cost of downtime or an outage is no longer just a technical inconvenience; it's a $600 billion wake up call for global businesses. As our digital ecosystems become  more interconnected, each touchpoint introduces new risks and multiplies the consequences when things go wrong. And the data is clear: aggregate downtime costs  for Global 2,000 companies have surged 50% since 2024, reaching a staggering $600 billion ...

Deloitte found that 74% of enterprises expect to deploy agentic AI solutions in the next 24 months. However, the rush to deployment is outpacing foundational work, though. Only 21% of enterprises have fully formed agent governance models in place. The result? AI agents deployed without guidance or governance begin to function as fragmented islands of complexity ...

Cloud spending is no longer viewed as a passthrough IT expense, but as a strategic financial lever that directly impacts innovation capacity, profitability and enterprise resilience, according to the CFO Cloud Cost Optimization Report from Azul ...

As AI moves from generating responses to performing actions, the need for trust increases exponentially. And as organizations enlist AI agents for increasingly sophisticated business processes, trust is going to be the single most important theme for spurring adoption. What can organizations do to build trustworthy AI agents? ...

I've spent a lot of time in the channel, and one thing I keep coming back to is this: a partner program is only as good as what it looks like in the field. Many programs look great on paper, but when a partner is in front of a customer navigating a complex hybrid environment or trying to make the case for AI-powered observability, the gap between what a vendor promises and what it actually delivers becomes very clear, very fast ...

Enterprises today operate in a real-time environment where uninterrupted access to trusted data has become a baseline expectation for users, applications and automated systems. Traditional DataOps models, built on manual effort and human triage, cannot keep pace with this always active demand. AI agents are emerging as the operational backbone, ensuring consistent data availability, reinforcing trustworthiness and enabling a level of scale that manual processes cannot achieve ...

For decades, trust in the digital workplace rested on familiar signals. We trusted faces on video calls, voices on the phone, and emails that appeared to come from people we knew. These cues felt human and intuitive. They anchored how decisions were made, approvals were granted, and access was authorized. AI-powered deepfakes have quietly broken that model ...

Cloud migration was supposed to be a one-way door. For most enterprises, it turns out it isn't. Cloud data repatriation is a real and growing trend. A new survey ... finds that 89% of organizations plan to expand their on-premises infrastructure footprint over the next two years — and 75% have already moved at least some workloads back from public cloud in the past 24 months. The findings point to a broad rethinking of where data belongs ...

Over the past few years, large language models (LLMs) have revolutionized the software industry. Given their ability to excel at multi-step reasoning, LLMs have helped enterprises streamline workflows and adapt to the unknown. However, employing such models comes with sky-high costs, latency issues, and limited flexibility. In the realm of IT operations, it is generally wiser to employ smaller, domain-specific models instead ...