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New Relic Introduces New Integrations with GitHub

New Relic announced three new integrations with GitHub that boost developer productivity and experience to accelerate innovation. 

The innovations, led by a new AI integration that automatically correlates software vulnerabilities and implements fixes, deliver intelligent observability insights where developers already work. Together, the longtime partners are helping GitHub Copilot’s more than 20 million users fast-track development, reduce downtime, and simplify workflows through automation.

“Agentic AI is everywhere, but developers aren’t yet seeing the productivity results they expected,” said New Relic Head of AI Camden Swita. “To unlock AI’s full potential, development teams need intelligent observability in the tools they use everyday. With our latest integrations with GitHub, we are continuing to deliver on our vision of bringing intelligent observability across the tech ecosystem.”

With the New Relic Security RX integration for GitHub Copilot, development teams can gather runtime and build time context of software vulnerabilities to understand the risks of both the security issue and the potential fix. Providing security in context helps distinguish real-world exposure from noise, gives a better understanding of the scope of the problem, and reduces manual research and triaging to prioritize fixing issues that could pose a real risk that's live in production versus what's merely sitting in a repository. The analysis generated via the New Relic Security RX integration for GitHub Copilot can then help create a clear remediation plan that will automatically initiate a GitHub issue containing impact details, testing and verification steps, and acceptance criteria. GitHub Copilot then generates a pull request with all necessary context for engineers, providing insights for faster resolution and reduced toil for developers.

New Relic’s new instrumentation assistant makes GitHub Copilot a more holistic and trustworthy solution-builder. The solution detects and resolves missing instrumentation at deployment by calling GitHub Copilot to implement full coverage directly in pull requests. The integration helps GitHub Copilot users generate more complete solutions. Instead of just a functional code snippet, the user would receive a more fully-realized service that includes observability. When creating or updating a service, GitHub Copilot doesn't just write the backend logic - it instruments the full-stack by including APM, custom attributes for business logic, a GitHub Action for Change Tracking integration with New Relic, and even the Browser agent for front-end visibility.

With the integration between New Relic Service Architecture Intelligence and GitHub, developers can import rich data from GitHub accounts directly into New Relic. The functionality helps developers improve velocity and automate configuration setup. 

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New Relic Introduces New Integrations with GitHub

New Relic announced three new integrations with GitHub that boost developer productivity and experience to accelerate innovation. 

The innovations, led by a new AI integration that automatically correlates software vulnerabilities and implements fixes, deliver intelligent observability insights where developers already work. Together, the longtime partners are helping GitHub Copilot’s more than 20 million users fast-track development, reduce downtime, and simplify workflows through automation.

“Agentic AI is everywhere, but developers aren’t yet seeing the productivity results they expected,” said New Relic Head of AI Camden Swita. “To unlock AI’s full potential, development teams need intelligent observability in the tools they use everyday. With our latest integrations with GitHub, we are continuing to deliver on our vision of bringing intelligent observability across the tech ecosystem.”

With the New Relic Security RX integration for GitHub Copilot, development teams can gather runtime and build time context of software vulnerabilities to understand the risks of both the security issue and the potential fix. Providing security in context helps distinguish real-world exposure from noise, gives a better understanding of the scope of the problem, and reduces manual research and triaging to prioritize fixing issues that could pose a real risk that's live in production versus what's merely sitting in a repository. The analysis generated via the New Relic Security RX integration for GitHub Copilot can then help create a clear remediation plan that will automatically initiate a GitHub issue containing impact details, testing and verification steps, and acceptance criteria. GitHub Copilot then generates a pull request with all necessary context for engineers, providing insights for faster resolution and reduced toil for developers.

New Relic’s new instrumentation assistant makes GitHub Copilot a more holistic and trustworthy solution-builder. The solution detects and resolves missing instrumentation at deployment by calling GitHub Copilot to implement full coverage directly in pull requests. The integration helps GitHub Copilot users generate more complete solutions. Instead of just a functional code snippet, the user would receive a more fully-realized service that includes observability. When creating or updating a service, GitHub Copilot doesn't just write the backend logic - it instruments the full-stack by including APM, custom attributes for business logic, a GitHub Action for Change Tracking integration with New Relic, and even the Browser agent for front-end visibility.

With the integration between New Relic Service Architecture Intelligence and GitHub, developers can import rich data from GitHub accounts directly into New Relic. The functionality helps developers improve velocity and automate configuration setup. 

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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 ...

For years, DevOps teams operated under a simple assumption: collect enough telemetry, and you can find and fix any problem. That assumption is breaking down. Modern enterprises now operate across microservices, hybrid cloud environments, APIs, Kubernetes, and highly automated delivery pipelines. Releases happen continuously, dependencies shift constantly, and failures spread faster than teams can diagnose them ...

New Relic surveyed IT and engineering leaders from the media and entertainment (M&E) sector to understand what's working — and where challenges persist with their observability practices. The findings reveal how M&E organizations are navigating rising platform complexity, audience expectations, and AI-driven change. Below are five takeaways that stand out ...

Let me start with something I've seen play out more times than I can count. A team hits a wall with the cloud. Costs creep up, then spike. Performance starts to feel inconsistent. Someone in finance asks a simple question like "why did this double?" and nobody has a clean answer ... Maybe this isn't the right place for everything. That realization feels like a breakthrough, like you've identified the problem. In reality, you've just identified the starting line ...

In MEAN TIME TO INSIGHT Episode 24, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses network observability tool sprawl ... 

In cloud-native systems, scaling is often as simple as moving a slider. For on-premise databases, the stakes are different. Over-provisioning hardware is expensive. Under-provisioning leads to performance bottlenecks that are difficult to fix once the equipment is in the rack ...

When most people think about cybersecurity, they picture firewalls, encryption, and access controls — technical tools designed to protect systems and data. But beneath the technology lies a deeper set of principles about trust, decision-making, and resilience ... The best leaders don't eliminate risk. They manage it intelligently. And in many ways, cybersecurity offers a surprisingly useful playbook for doing exactly that ...