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New Relic Introduces AI Coding Observability

New Relic announced development of a new open-source feature called New Relic AI Coding Observability, an observability solution designed specifically for AI-assisted software development. 

New Relic AI Coding Observability extends production-grade monitoring directly into the coding phase of the software lifecycle, transforming fragmented, unmonitored AI usage into a highly governed, optimized and auditable enterprise advantage.

“You can’t manage what you can’t see. AI coding assistants are having a measurable impact on businesses, but without real-time oversight into how they’re behaving, organizations are scaling risk as fast as they’re scaling output,” said New Relic Chief Product Officer Brian Emerson. “New Relic AI Coding Observability will close this gap, removing barriers to quality innovation that succeeds in production.”

New Relic AI Coding Observability is being designed to future-proof development strategies and introduce a unified, vendor-neutral pane of glass that normalizes telemetry across the major AI coding assistants and correlates it seamlessly with existing production infrastructure.

Key features and benefits of New Relic AI Coding Observability will include:

  • Gain insights into code development actions - Teams will be able to move away from blind trust in their AI coding tools to gaining comprehensive insights into how these tools are actually behaving as applications and services are developed. 
  • Exercise cost control - AI coding assistants are a rapidly growing line item, yet most organizations treat them as an unmonitored expense. New Relic AI Observability will allow teams to track AI spend and eliminate black box invoices, and forecast spend against budgets and alert before thresholds are hit.
  • Enhance productivity metrics - To understand productivity gains from coding assistant use, the capability will replace anecdotal success stories with hard data and catch inefficiencies and hidden failure modes.
  • Ensure security and compliance - Local-only / zero-outbound mode will run queries entirely within the user’s private network, guaranteeing data sovereignty, privacy and regulatory compliance.
  • Receive strong code transparency - Will eliminate black-box AI skepticism by providing fully readable, open-source and source-available code, empowering engineering and security teams to independently verify data privacy protocols and AI reasoning with certainty.
  • Avoid vendor lock-in - Backed natively by the OpenTelemetry protocol and Model Context Protocol (MCP), the feature will deliver true vendor-neutrality that allows organizations to seamlessly port their telemetry data and AI workflows across any cloud ecosystem or language model provider.
  • Provides broad coding assistant coverage - Will support Claude Code, Cursor, GitHub Copilot, Windsurf and Amazon Q.

New Relic AI Coding Observability will be available as an open-source feature on June 23 at no additional cost. Standard New Relic ingest rates apply, with local-only mode also available soon.

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For decades, traditional observability tools have forced IT professionals to sift through endless dashboards, turning a flood of alerts into actionable insight and driving widespread burnout. While enterprises adopted AI to eliminate this friction, most still deploy it reactively rather than proactively ... As systems become more complex and IT moves to the center of business strategy, a reactive posture is unsustainable ...

Ask an operations team what they monitor and you will hear a familiar list: latency, errors, throughput, uptime. Those signals still matter for AI, but they no longer answer the most important question. As Krishna Sai, CTO of SolarWinds, puts it: "Traditional observability tells you a system is running. AI observability tells you whether it's still reasoning soundly. Those are completely different questions" ...

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New Relic Introduces AI Coding Observability

New Relic announced development of a new open-source feature called New Relic AI Coding Observability, an observability solution designed specifically for AI-assisted software development. 

New Relic AI Coding Observability extends production-grade monitoring directly into the coding phase of the software lifecycle, transforming fragmented, unmonitored AI usage into a highly governed, optimized and auditable enterprise advantage.

“You can’t manage what you can’t see. AI coding assistants are having a measurable impact on businesses, but without real-time oversight into how they’re behaving, organizations are scaling risk as fast as they’re scaling output,” said New Relic Chief Product Officer Brian Emerson. “New Relic AI Coding Observability will close this gap, removing barriers to quality innovation that succeeds in production.”

New Relic AI Coding Observability is being designed to future-proof development strategies and introduce a unified, vendor-neutral pane of glass that normalizes telemetry across the major AI coding assistants and correlates it seamlessly with existing production infrastructure.

Key features and benefits of New Relic AI Coding Observability will include:

  • Gain insights into code development actions - Teams will be able to move away from blind trust in their AI coding tools to gaining comprehensive insights into how these tools are actually behaving as applications and services are developed. 
  • Exercise cost control - AI coding assistants are a rapidly growing line item, yet most organizations treat them as an unmonitored expense. New Relic AI Observability will allow teams to track AI spend and eliminate black box invoices, and forecast spend against budgets and alert before thresholds are hit.
  • Enhance productivity metrics - To understand productivity gains from coding assistant use, the capability will replace anecdotal success stories with hard data and catch inefficiencies and hidden failure modes.
  • Ensure security and compliance - Local-only / zero-outbound mode will run queries entirely within the user’s private network, guaranteeing data sovereignty, privacy and regulatory compliance.
  • Receive strong code transparency - Will eliminate black-box AI skepticism by providing fully readable, open-source and source-available code, empowering engineering and security teams to independently verify data privacy protocols and AI reasoning with certainty.
  • Avoid vendor lock-in - Backed natively by the OpenTelemetry protocol and Model Context Protocol (MCP), the feature will deliver true vendor-neutrality that allows organizations to seamlessly port their telemetry data and AI workflows across any cloud ecosystem or language model provider.
  • Provides broad coding assistant coverage - Will support Claude Code, Cursor, GitHub Copilot, Windsurf and Amazon Q.

New Relic AI Coding Observability will be available as an open-source feature on June 23 at no additional cost. Standard New Relic ingest rates apply, with local-only mode also available soon.

The Latest

For decades, traditional observability tools have forced IT professionals to sift through endless dashboards, turning a flood of alerts into actionable insight and driving widespread burnout. While enterprises adopted AI to eliminate this friction, most still deploy it reactively rather than proactively ... As systems become more complex and IT moves to the center of business strategy, a reactive posture is unsustainable ...

Ask an operations team what they monitor and you will hear a familiar list: latency, errors, throughput, uptime. Those signals still matter for AI, but they no longer answer the most important question. As Krishna Sai, CTO of SolarWinds, puts it: "Traditional observability tells you a system is running. AI observability tells you whether it's still reasoning soundly. Those are completely different questions" ...

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 ...