New Relic this week made available a preview of an integration with the artificial intelligence (AI) agents it created for its observability platform with the AI agents that GitHub has developed.

Nic Benders, chief technical strategist for New Relic, said AI agents that the company has developed will now be able to create an issue that GitHub Copilot will be able to use to improve an application. GitHub Copilot will analyze the issue, draft a fix, and submit a draft pull request for human review. The New Relic platform will then validate the change once it is merged.

In effect, the New Relic and GitHub agents are creating a virtuous cycle to address incidents and performance as they arise, said Benders.

Just as importantly, the New Relic agents are creating issues in a semi-structured format that is human readable, so DevOps teams will have the option of either assigning that task to a human developer or GitHub Copilot, he added.

New Relic is making this capability initially available to eligible accounts that are also Copilot Pro+ or Copilot Enterprise users. Launched at the Microsoft Build 2025 conference, the GitHub Copilot coding agent is currently available as a preview to GitHub Copilot Enterprise customers and GitHub Copilot Pro+ users.

While DevOps teams are making greater use of AI tools, it might be a few years yet before they are fully mastered, noted Benders. Using AI tools to generate code is one thing but integrating AI agents into DevOps workflows is a more complex endeavor, he noted.

Nevertheless, a recent Futurum Group survey finds 41% of respondents expect AI tools and platforms will be used to generate, review and test code, while 39% plan to make use of AI models based on machine learning algorithms. The challenge now is identifying the specific tasks that might best be handled by an AI agent to reduce the overall amount of toil that goes into software engineering, said Benders.

Ultimately, AI agents will also make DevOps teams less dependent on a confusing set of observability dashboards as AI agents begin to proactively discover issues, hopefully, before they have any significant adverse impact on application environments.

Each organization will need to determine for themselves the degree to which they are comfortable relying on AI to build and deploy applications, but at this point, it’s more a question of how much rather than if. There are clearly productivity gains to be had so long as the work of AI agents remains closely supervised by DevOps engineers that have the expertise required to ensure that application quality is maintained and enhanced.

In the meantime, DevOps teams should be aggressively experimenting with AI tools and platforms, said Benders. Like most powerful tools, it requires knowledge and expertise to get the most out of AI that only comes with hands-on experience, he added.

After all, the DevOps goal has always been to automate as many tasks and workflows as possible. AI agents are only the latest in a long series of tools and platforms that enable DevOps teams to achieve that goal.