New Relic Ground Truth

Your AI doesn’t need more telemetry. It needs operational context.

For more than a decade, observability has transformed how engineering teams operate modern software. Metrics, events, logs, and traces have given teams unprecedented visibility into increasingly complex systems, making it possible to detect issues faster, investigate incidents more effectively, and keep digital experiences running reliably.

Today, AI is transforming software operations just as dramatically.

Engineering teams are increasingly relying on AI assistants to investigate incidents, explain system behavior, recommend remediation steps, generate code, and automate operational tasks. As these capabilities mature, AI is evolving from a productivity tool into an active participant in software operations.

But a fundamental challenge remains.

AI has access to enormous amounts of telemetry, yet it often struggles to understand what that data actually means. Individual metrics, logs, traces, and events provide valuable signals, but they rarely tell the complete operational story. Instead, AI must piece together relationships between services, infrastructure, deployments, ownership, incidents, and business impact before it can deliver a recommendation engineers can trust.

That process is expensive, time consuming, and often inconsistent.

Many AI systems repeatedly query multiple tools, stitch together disconnected information, and attempt to reconstruct the same operational context every time a question is asked. The result is higher token consumption, increased latency, duplicated engineering effort, and recommendations that still require significant human validation.

The challenge isn’t a lack of data.

It’s a lack of understanding at the operational level.

Why more data doesn’t create better AI

The irony of modern enterprise software is that most organizations don’t suffer from a shortage of data. They suffer from an abundance of it.

Every deployment, infrastructure component, application, and service continuously generates metrics, logs, traces, events, topology information, dependency data, and change history. Individually, these signals are incredibly valuable. Together, they contain the information needed to understand almost any operational problem.

The challenge is that AI rarely receives that information as a complete operational picture.

Imagine asking an AI assistant a simple question:

“Why did checkout latency increase after yesterday’s deployment?”

The answer likely exists somewhere within your observability platform. But before AI can respond, it often needs to retrieve deployment history, examine service dependencies, identify recent infrastructure changes, correlate traces, review incident history, determine service ownership, and understand business impact.

Each additional query adds latency, consumes tokens, and increases the likelihood that important context is missed or interpreted incorrectly. As organizations deploy more AI assistants and autonomous agents, every system ends up repeating much of the same work.

The problem isn’t that AI lacks intelligence.

It’s that every AI workflow starts without a shared understanding of the environment.

That’s the problem New Relic Ground Truth was built to solve.

Rather than simply exposing telemetry, New Relic Ground Truth continuously transforms telemetry, dependencies, incidents, changes, ownership, and business context into a unified operational model that both engineers and AI can understand. Instead of forcing every AI workflow to rebuild context from scratch, Ground Truth creates trusted operational intelligence once and makes it available wherever AI operates.

The result is faster investigations, more accurate recommendations, lower AI operating costs, and a stronger foundation for Autonomous Operations.

The Next Evolution of operational intelligence

Every major advancement in IT operations has expanded our ability to answer increasingly sophisticated questions.

Traditional monitoring helped answer, “Is something broken?”

Observability advanced that conversation by helping teams answer, “Why is it broken?”

AI introduces an even more ambitious question:

“Can software understand enough about my environment to investigate problems, recommend actions, and eventually resolve issues with confidence?”

Answering that question requires something observability platforms were never originally designed to provide.

It requires operational intelligence.

Operational intelligence goes beyond collecting telemetry. It continuously connects relationships across applications, infrastructure, services, deployments, incidents, ownership, and business systems to create a shared understanding of how an environment actually operates.

Experienced engineers naturally think this way. They recognize how a recent deployment may relate to an increase in latency, understand which downstream dependency is contributing to customer impact, and know which team owns the affected service.

AI must develop that same understanding before it can consistently produce recommendations organizations are willing to trust.

This is where New Relic Ground Truth represents a significant evolution.

Rather than asking every AI assistant to independently reconstruct operational context through dozens of API calls and telemetry queries, Ground Truth provides a reusable layer of trusted operational intelligence that can be shared across AI assistants, automation workflows, and Autonomous Operations.

Instead of spending time gathering context, AI can spend time solving problems.

Get Context Once. Reuse It Everywhere.

This is where New Relic Ground Truth fundamentally changes the architecture of AI-powered operations.

Instead of requiring every AI assistant to reconstruct operational context from dozens of independent telemetry queries, Ground Truth continuously builds a trusted operational model that represents how your environment actually operates.

Applications, infrastructure, services, deployments, incidents, ownership, dependencies, and business context are continuously connected into reusable operational intelligence.

Every AI workflow begins from that same shared understanding.

Rather than collecting information first and reasoning second, AI starts with operational context already in place.

This seemingly simple shift delivers meaningful advantages across engineering organizations.

AI investigations become faster because context has already been assembled.

Recommendations become more accurate because they are grounded in operational relationships instead of isolated telemetry.

Organizations reduce AI operating costs by eliminating repetitive telemetry queries, lowering token consumption, and minimizing duplicated processing across AI workflows.

Perhaps most importantly, engineering teams gain consistency. Every AI assistant, automation workflow, and future autonomous agent works from the same trusted operational understanding instead of building its own interpretation of reality.

That consistency becomes increasingly valuable as organizations expand from individual AI copilots to enterprise-scale Autonomous Operations.

Operational intelligence becomes a shared foundation rather than something every AI system must recreate independently.

That’s the architectural difference New Relic Ground Truth delivers.

Better Context Creates trusted Decisions

The success of AI in operations won’t be determined by which model organizations choose. It will be determined by the quality of the operational understanding those models receive.

An AI assistant that only sees telemetry can identify symptoms. An AI assistant grounded in trusted operational intelligence can explain relationships, identify likely causes, understand business impact, and recommend the next best action with far greater confidence.

Consider a common production incident.

Telemetry shows that checkout latency has increased.

Without operational context, AI may identify elevated database latency or a spike in CPU utilization, but it still lacks the broader understanding needed to explain why the problem occurred or what should happen next.

Grounded in trusted operational intelligence, the same AI can recognize that a deployment occurred minutes before the latency increase, identify the downstream service affected, understand which engineering team owns the application, correlate similar historical incidents, and explain why rolling back the deployment is the most likely resolution.

Both AI systems analyzed the same telemetry.

Only one understood the operational context.

That distinction is what transforms AI from an intelligent assistant into a trusted operational partner.

The Foundation for Autonomous Operations

As organizations mature their AI strategies, they naturally progress from experimentation to automation.

AI begins by answering questions.

Then it recommends actions.

Eventually, it orchestrates workflows and executes operational tasks with appropriate governance and human oversight.

Each step requires greater confidence.

Organizations cannot safely automate operational decisions if every AI workflow reconstructs its own understanding of the environment. Trust depends on consistency, evidence, and shared operational context.

This is why New Relic Ground Truth is foundational to New Relic’s vision for Autonomous Operations.

By continuously creating and maintaining a trusted operational model, Ground Truth provides the intelligence layer that powers AI recommendations, automation, and future autonomous workflows across the New Relic platform. Instead of each AI capability independently gathering telemetry and assembling context, they begin with a shared understanding of the operational environment.

That approach delivers benefits beyond better AI responses.

Engineering teams spend less time manually validating recommendations.

AI systems consume fewer resources because they reuse operational context rather than repeatedly rebuilding it.

Organizations gain more consistent outcomes because every AI workflow operates from the same trusted operational model.

As AI adoption accelerates, these efficiencies become increasingly important. Reducing repetitive telemetry queries, minimizing token consumption, and eliminating duplicated engineering effort doesn’t simply improve performance. It creates an operational model that is more scalable, more economical, and easier to govern.

In other words, New Relic Ground Truth doesn’t just help AI make better decisions.

It creates the trusted operational foundation that makes Autonomous Operations possible.

Looking Ahead

The next generation of software operations won’t be defined by organizations with the largest language models or the greatest number of AI agents.

It will be defined by organizations that give those AI systems the operational understanding they need to reason, recommend, and act with confidence.

Observability changed how engineers understand software.

Trusted operational intelligence will change how AI understands it.

By transforming telemetry into reusable operational intelligence, New Relic Ground Truth establishes the missing layer between observability and Autonomous Operations. It enables AI to move beyond retrieving information toward delivering trusted, explainable, and actionable operational insights.

The future of operations isn’t about collecting more telemetry.

It’s about giving AI the understanding to turn that telemetry into confident action.

That’s the promise of New Relic Ground Truth.