New Relic and GitHub Copilot AI Agents Join Forces to Automate DevOps Workflows

New Relic Integrates AI Agents with GitHub Copilot Coding Agent

New Relic has introduced a preview integration between its AI agents for observability and GitHub’s Copilot coding agent. This integration allows New Relic’s AI agents to create issues that GitHub Copilot can use to improve applications. Once GitHub Copilot analyzes an issue, it drafts a fix and submits a pull request for human review. After merging, New Relic’s platform validates the change to ensure it works as intended.

This creates a continuous feedback loop where New Relic and GitHub AI agents work together to detect and resolve incidents or performance problems as they occur. The issues created by New Relic’s agents are semi-structured and human-readable, giving DevOps teams flexibility to assign tasks either to developers or to GitHub Copilot.

Availability and Access

The integration is initially available to New Relic users with eligible accounts who also have GitHub Copilot Pro+ or Enterprise subscriptions. GitHub Copilot’s coding agent was launched as a preview at Microsoft Build 2025 and is currently accessible to Copilot Enterprise and Pro+ customers.

AI’s Role in DevOps Workflows

While AI tools are becoming more common in DevOps, fully integrating AI agents into workflows remains complex. Generating code with AI is one step; embedding AI deeply into software development and operations is another. According to a recent survey by the Futurum Group, 41% of respondents expect AI to be used for generating, reviewing, and testing code, while 39% plan to leverage machine learning-based AI models.

The key challenge now is identifying which tasks AI agents can handle best to reduce repetitive work and improve efficiency. Over time, AI agents could reduce reliance on traditional observability dashboards by proactively detecting issues before they impact applications.

Balancing AI Automation with Human Oversight

Organizations must decide how much they trust AI agents to build and deploy applications. The question is increasingly about the extent of AI’s role rather than if it should be used at all. Productivity gains are achievable when AI-generated work is carefully supervised by experienced DevOps engineers who maintain application quality.

In the meantime, DevOps teams should actively experiment with AI tools to gain practical experience. Like any powerful tool, AI requires hands-on use to understand its strengths and limitations. Automating tasks has always been a core DevOps goal, and AI agents are the latest tool helping teams move closer to that objective.