
New Relic further enhanced its AIOps capabilities with recommended alerts. This provides the ability to quickly detect and easily resolve alert coverage gaps by using AI to identify anomalous behavior, determine areas of the technology stack that aren’t being monitored, and recommend new alerts to engineers.
An observability solution that reduces the need to manually build numerous alert conditions using AI makes it easier to understand which signals are most important or what thresholds indicate performance problems. Now, every engineer, regardless of their experience level, can eliminate alerting blindspots—empowering them to detect, resolve, and respond to issues faster.
“In an increasingly dynamic landscape, it’s easy for engineering teams to be overwhelmed by the need to configure alerts across different layers of the technology stack, especially since manually creating alert policies can be time- and resource-intensive. This can cause enormous gaps in the team’s alerting policies, leaving them blind and incapable of responding quickly and confidently when things break,” said New Relic Chief Product Officer Manav Khurana. “We designed New Relic recommended alerts to remove those barriers, so teams have the alerts they need to proactively monitor their stack, diagnose incidents and prioritize them for immediate action before it impacts their customers, business, and bottom line.”
Powered by AIOps, New Relic recommended alerts streamlines alerting with its alert coverage gaps feature, which continuously and automatically highlights areas in an organization’s technology stack that are missing alert coverage across application performance monitoring (APM), mobile and browser entities. Then, New Relic fills alerting gaps by recommending new alerts with pre-populated alert conditions, such as error percentage or response time. Using the recommendations as a starting point, engineers can also customize their alerts by implementing additional parameters to tailor the alert conditions and drive even better coverage for the team.
New Relic recommended alerts builds upon New Relic AI, a suite of AIOps capabilities that understands historical alerts and applies machine learning (ML) and AI to significantly reduce alert noise, enrich incidents with context, and provide intelligence and automation to engineering teams in real-time. With New Relic AI, engineers can detect, diagnose and resolve incidents faster, and continuously improve incident management workflow.
Coming soon, engineering teams will also be able to utilize New Relic Grok (currently in early access) to further enhance alerting by asking questions in natural language like “Hey Grok, what are the uncovered entities that I should be monitoring and what are the recommended conditions for this alert?” which will enhance the team's alert strategy and provide engineers with even better alert coverage.
Currently available as part of the New Relic platform, recommended alerts are now available at no additional cost to existing New Relic users.
The Latest
Enterprises today operate in a real-time environment where uninterrupted access to trusted data has become a baseline expectation for users, applications and automated systems. Traditional DataOps models, built on manual effort and human triage, cannot keep pace with this always active demand. AI agents are emerging as the operational backbone, ensuring consistent data availability, reinforcing trustworthiness and enabling a level of scale that manual processes cannot achieve ...
For decades, trust in the digital workplace rested on familiar signals. We trusted faces on video calls, voices on the phone, and emails that appeared to come from people we knew. These cues felt human and intuitive. They anchored how decisions were made, approvals were granted, and access was authorized. AI-powered deepfakes have quietly broken that model ...
Cloud migration was supposed to be a one-way door. For most enterprises, it turns out it isn't. Cloud data repatriation is a real and growing trend. A new survey ... finds that 89% of organizations plan to expand their on-premises infrastructure footprint over the next two years — and 75% have already moved at least some workloads back from public cloud in the past 24 months. The findings point to a broad rethinking of where data belongs ...
Over the past few years, large language models (LLMs) have revolutionized the software industry. Given their ability to excel at multi-step reasoning, LLMs have helped enterprises streamline workflows and adapt to the unknown. However, employing such models comes with sky-high costs, latency issues, and limited flexibility. In the realm of IT operations, it is generally wiser to employ smaller, domain-specific models instead ...
For years, DevOps teams operated under a simple assumption: collect enough telemetry, and you can find and fix any problem. That assumption is breaking down. Modern enterprises now operate across microservices, hybrid cloud environments, APIs, Kubernetes, and highly automated delivery pipelines. Releases happen continuously, dependencies shift constantly, and failures spread faster than teams can diagnose them ...
New Relic surveyed IT and engineering leaders from the media and entertainment (M&E) sector to understand what's working — and where challenges persist with their observability practices. The findings reveal how M&E organizations are navigating rising platform complexity, audience expectations, and AI-driven change. Below are five takeaways that stand out ...
Let me start with something I've seen play out more times than I can count. A team hits a wall with the cloud. Costs creep up, then spike. Performance starts to feel inconsistent. Someone in finance asks a simple question like "why did this double?" and nobody has a clean answer ... Maybe this isn't the right place for everything. That realization feels like a breakthrough, like you've identified the problem. In reality, you've just identified the starting line ...
In MEAN TIME TO INSIGHT Episode 24, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses network observability tool sprawl ...
In cloud-native systems, scaling is often as simple as moving a slider. For on-premise databases, the stakes are different. Over-provisioning hardware is expensive. Under-provisioning leads to performance bottlenecks that are difficult to fix once the equipment is in the rack ...
When most people think about cybersecurity, they picture firewalls, encryption, and access controls — technical tools designed to protect systems and data. But beneath the technology lies a deeper set of principles about trust, decision-making, and resilience ... The best leaders don't eliminate risk. They manage it intelligently. And in many ways, cybersecurity offers a surprisingly useful playbook for doing exactly that ...