AI coding assistants like GitHub Copilot have dramatically increased developer velocity, allowing engineering teams to ship code faster than ever before. However, rapidly deploying AI-generated software brings new operational challenges. According to New Relic's 2026 State of AI Coding report, while 94% of engineering leaders rate AI-generated code as higher quality during review, 78% report an increase in production incidents after deployment.
Today, at GitHub Universe 2026, New Relic announced a suite of new integrations for GitHub Copilot and GitHub Model Context Protocol (MCP) Server designed to bridge the gap between AI-assisted development and production reliability. By bringing continuous telemetry directly into the developer workflow—from local integrated development environment (IDE) coding to automated pull request (PR) verification and incident response—engineers and AI agents can collaborate in a unified ecosystem to ensure AI velocity does not compromise system trust.
Key takeaways
- New Relic Change Tracking for GitHub Copilot continuously evaluates pull requests against real-time performance data, automatically isolating regressions and routing fixes directly to GitHub Issues and Copilot
- New Relic Autopilot for GitHub MCP Server connects New Relic Autopilot with GitHub codebase context to provide guided, context-aware incident triage and resolution
- New Relic Preflight for GitHub Copilot brings local IDE and command-line interface (CLI) visibility into AI model usage, latency, token consumption, and cost optimization before code is pushed to production
New Relic Change Tracking for GitHub Copilot: Closed-loop telemetry for PRs
When code velocity scales exponentially, traditional post-deploy verification and manual triage create severe bottlenecks. New Relic Change Tracking for GitHub Copilot creates an automated, closed-loop workflow between production telemetry and developer remediation with the following:
- Monitoring plan before merge: Reads the PR diff prior to merging and automatically posts a comprehensive monitoring plan directly as a PR comment
- Six-checkpoint verdict after every deploy: Following deployment, evaluates performance at six distinct checkpoints (10 minutes, 30 minutes, 1 hour, 2 hours, 24 hours, and 72 hours), delivering clear verdicts: Good, Inconclusive, or Regression detected
- Auto-generated fix PR on regression: If a regression is detected, a GitHub Issue is automatically logged. GitHub Copilot reads the issue, generates a fix PR, and New Relic Change Tracking restarts the verification cycle upon review
Plus, we have exciting enhancements on the way: Streamlined integration onboarding, the ability to post a monitoring plan as a PR comment, custom scheduling for monitoring runs, and more.
New Relic Autopilot for GitHub MCP Server: Transforming detection into resolution
When an incident fires, the relevant code change lives in GitHub while production signals live in New Relic. Manually correlating them creates a heavy cognitive tax and stalls resolution. By integrating New Relic Autopilot with the GitHub MCP Server, engineering teams pair full repository history with live telemetry to transform detection into autonomous resolution using:
- Unified AI context: Pipes GitHub releases, PRs, and commit history directly into New Relic Autopilot, supplying full codebase context to maximize model accuracy and eliminate hallucinatory fixes
- Frictionless resolution: Eliminates context-switching by displaying development data alongside performance metrics in a single interface, allowing engineers to troubleshoot without leaving New Relic
- Autonomous triage: Agentically correlates repository events with telemetry anomalies to pinpoint the exact PR or line of code that triggered a regression, dramatically reducing mean time to resolution (MTTR)
New Relic Preflight for GitHub Copilot: Removing hidden performance debt in local workflows
Lack of transparency into local AI usage and unmonitored model spend create hidden performance debt and budget risks before code ever reaches production. New Relic Preflight for GitHub Copilot shifts AI observability left into the developer's active workflow with:
- Local-first insights: Delivers real-time visibility inside the developer's IDE and CLI, tracking token consumption, execution latency, real-time spend, and cost forecasts as code is written
- Unified multi-model LLM monitoring: Normalizes telemetry across diverse LLMs and tools into a single view for side-by-side performance, latency, and cost evaluations
- Enterprise observability & cost control: Bridges local telemetry directly to New Relic dashboards for team-wide alerting, budget forecasting, and long-term analytics without interrupting local flow state
Learn more
To discover more about how New Relic is bringing real-time observability to AI-assisted development:
- Visit us at booth #114 at GitHub Universe in San Francisco, October 28-29
- Check out docs.newrelic.com to set up New Relic Change Tracking for GitHub Copilot
- Try New Relic Preflight for GitHub Copilot
Les opinions exprimées sur ce blog sont celles de l'auteur et ne reflètent pas nécessairement celles de New Relic. Toutes les solutions proposées par l'auteur sont spécifiques à l'environnement et ne font pas partie des solutions commerciales ou du support proposés par New Relic. Veuillez nous rejoindre exclusivement sur l'Explorers Hub (support.newrelic.com) pour toute question et assistance concernant cet article de blog. Ce blog peut contenir des liens vers du contenu de sites tiers. En fournissant de tels liens, New Relic n'adopte, ne garantit, n'approuve ou n'approuve pas les informations, vues ou produits disponibles sur ces sites.
