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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. 

The Latest

AI observability serves several roles across an organization. AI engineers use it to build, evaluate, and improve AI systems. SREs use it to operate those systems reliably and efficiently in production, including managing their cost. Leaders need it to connect AI behavior and operational performance to business outcomes. The common need is a clear understanding of whether AI systems are delivering the intended result ...

Part 4 outlines the challenges enterprise face when adopting AI observability. The main challenge is that AI complexity compounds at every stage of maturity. LLM-based applications introduce new behavior, dependencies, and quality considerations, while Agentic AI adds further failure modes as systems make decisions and take actions ...

Part 3 reports on the current state of AI observability. Most experts agree: enterprises are moving faster with AI than they are with the operational discipline needed to run it safely ...

When discussing AI Observability, one of the first questions that arises: Is this just an extension of traditional observability or is this a whole new type of tool? Part 2 compares traditional observability with AI observability ...

In this extensive 14-part series that will post over the next three weeks, experts across observability and AI will provide insight and opinions on the need for AI Observability, the challenges, the benefits, the current state, and predictions for the future ...

Amid rising complexity, organizations are moving quickly toward agent-driven operations: AgenticOps, according to The Impact of Agentic AI on Network Operations, a new report from Cisco ...

AI, automation, and enterprise software give organizations an extraordinary amount of leverage. Work that once required hours can happen in minutes. Information buried across systems can be analyzed almost instantly. Complex workflows can be coordinated across teams with far less manual intervention. That leverage creates a temptation to start with the technology ...

Everyone wants to talk about models. Compute clusters. Benchmark scores that climb a fraction of a point and make headlines anyway. It's an easy conversation to have because it's measurable. But it's also increasingly beside the point ...

For more than a decade, the default infrastructure play has been simple. Move more workloads to the cloud, retire on-premises gear and treat the data center as a cost to shrink. That logic held while public cloud offered near-limitless capacity at predictable prices, and AI demand is now testing both halves of that assumption ...

Most enterprise IT leaders can tell you exactly how many applications are running across their organization. They can surface a dashboard showing telecom spend by region, pull a report on SaaS license utilization, or call up a real-time view of mobile device inventory. The data is there, and IT leaders have largely delivered on the promise of visibility. Yet, in most organizations, that visibility has not translated into business value ...

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. 

The Latest

AI observability serves several roles across an organization. AI engineers use it to build, evaluate, and improve AI systems. SREs use it to operate those systems reliably and efficiently in production, including managing their cost. Leaders need it to connect AI behavior and operational performance to business outcomes. The common need is a clear understanding of whether AI systems are delivering the intended result ...

Part 4 outlines the challenges enterprise face when adopting AI observability. The main challenge is that AI complexity compounds at every stage of maturity. LLM-based applications introduce new behavior, dependencies, and quality considerations, while Agentic AI adds further failure modes as systems make decisions and take actions ...

Part 3 reports on the current state of AI observability. Most experts agree: enterprises are moving faster with AI than they are with the operational discipline needed to run it safely ...

When discussing AI Observability, one of the first questions that arises: Is this just an extension of traditional observability or is this a whole new type of tool? Part 2 compares traditional observability with AI observability ...

In this extensive 14-part series that will post over the next three weeks, experts across observability and AI will provide insight and opinions on the need for AI Observability, the challenges, the benefits, the current state, and predictions for the future ...

Amid rising complexity, organizations are moving quickly toward agent-driven operations: AgenticOps, according to The Impact of Agentic AI on Network Operations, a new report from Cisco ...

AI, automation, and enterprise software give organizations an extraordinary amount of leverage. Work that once required hours can happen in minutes. Information buried across systems can be analyzed almost instantly. Complex workflows can be coordinated across teams with far less manual intervention. That leverage creates a temptation to start with the technology ...

Everyone wants to talk about models. Compute clusters. Benchmark scores that climb a fraction of a point and make headlines anyway. It's an easy conversation to have because it's measurable. But it's also increasingly beside the point ...

For more than a decade, the default infrastructure play has been simple. Move more workloads to the cloud, retire on-premises gear and treat the data center as a cost to shrink. That logic held while public cloud offered near-limitless capacity at predictable prices, and AI demand is now testing both halves of that assumption ...

Most enterprise IT leaders can tell you exactly how many applications are running across their organization. They can surface a dashboard showing telecom spend by region, pull a report on SaaS license utilization, or call up a real-time view of mobile device inventory. The data is there, and IT leaders have largely delivered on the promise of visibility. Yet, in most organizations, that visibility has not translated into business value ...