New Relic announced New Relic Autopilot and New Relic Ground Truth capabilities to enable engineering teams to either deploy an automated SRE agent operated entirely by New Relic, or supercharge their existing, custom AI agents by natively integrating them with New Relic's observability data substrate.
"Operations are going headless. AI agents won't log in to view dashboards. They'll pull what they need through APIs, reason about it, and act," said Camden Swita, Head of AI at New Relic. "That's exactly what New Relic Ground Truth delivers: it grounds your own agents in the real truth of your systems, through APIs instead of UIs. And for teams who'd rather we run the agent, there's New Relic Autopilot, our expert agent operated for you. Both sit on the same rich data substrate, and either way the toil is reduced."
The New Relic Autopilot capability is an out-of-the-box automated SRE agent built on New Relic's data substrate that automatically triages incidents, identifies root causes, and scopes possible remediations. It starts with analysis the moment an alert fires, giving human responders a significant advantage early on in the response process and helping them hit service level objectives (SLOs).
Key features of New Relic Autopilot include:
- A Growing Team of Expert Agents and Specialized Tools: Features domain specialists in Kubernetes, tools optimized for Kafka troubleshooting and cross-stack root-cause analysis, with more coming soon. Every result is a well-structured account grounded in concrete data and expert insight.
- Learns Your Environment: New Relic Knowledge grounds conclusions in an organization's specific runbooks and retrospectives. External Model Context Protocol (MCP) connections to Jira and GitHub pull in code details to pin down problems.
- Long-Term Memory: Captures tribal knowledge and disperses it across the team. Memory can be scoped and managed to optimize agent accuracy.
- Works Where You Do: Features a new agentic user experience on the New Relic platform, in Slack, and via automated workflow actions triggered by a SEV1, after a deploy, or on a schedule.
The New Relic Ground Truth capability provides a suite of singular, exclusive tools for AI agents that gives the agents businesses already run, whether GitHub Copilot, Claude Code, AWS DevOps, or an orchestrator they’ve built, direct access to the deepest insights inside their New Relic data.
Key features of New Relic Ground Truth include:
- Exclusive, Agent-Optimized Insights: Exposes premium tools offering New Relic's richest insights that are difficult to get through public APIs or basic query tools.
- On Your Own Terms: Seamlessly works with the agents and orchestrators customers already run, including GitHub Copilot, Claude Code, AWS DevOps, or custom orchestrators.
- Efficiency at Scale: Designed to help agents discover insights rapidly with fewer tool calls, which can significantly improve the token efficiency.
New Relic Autopilot and New Relic Ground Truth are available summer 2026.
The Latest
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 ...
For more than a decade, the default infrastructure play has been simple. Move more workloads to the cloud, retire on-premises gear and treat the data center as a cost to shrink. That logic held while public cloud offered near-limitless capacity at predictable prices, and AI demand is now testing both halves of that assumption ...
Most enterprise IT leaders can tell you exactly how many applications are running across their organization. They can surface a dashboard showing telecom spend by region, pull a report on SaaS license utilization, or call up a real-time view of mobile device inventory. The data is there, and IT leaders have largely delivered on the promise of visibility. Yet, in most organizations, that visibility has not translated into business value ...
Government agencies are largely misjudging their level of AI governance as the technology permeates their organization, according to Racing to Deploy: How Public Sector AI Adoption is Moving Faster Than the Foundations Needed to Support It, a new report from SolarWinds ...