New Relic unveils Logs Intelligence to accelerate root cause analysis with AI
New Relic has announced Logs Intelligence, a series of AI-strengthened capabilities that automate the time and effort required to reduce mean time to resolution (MTTR) and extract critical insights from logs. Featured key innovations, like AI Log Alerts Summarization, transform how teams work with log data by providing automated analysis to generate a rapid hypothesis, accelerating time to root cause understanding and incident response.
“Distributed systems and AI tools generate logs at an unprecedented rate,” said New Relic CTO Siva Padisetty. “Traditional log management, the backbone of troubleshooting but a source of immense complexity and manual effort, becomes infinitely challenging at this scale. With Logs Intelligence, New Relic turns the wall of unstructured data into actionable insights — accelerating incident response, reducing MTTR, and freeing teams to focus on higher-value work.”
Systems can have more than 50GB daily logs per 100-node cluster, 10,000-plus log lines per transaction across services, and verbose model inference logs that burden AI workloads. Instead of engineers manually correlating logs and hunting for root causes, intelligent observability can automatically identify patterns, predict issues before they become outages, and provide instant context during incidents, ultimately increasing system stability and business uptime. Now a capability of New Relic’s Intelligent Observability Platform, New Relic Logs Intelligence transforms a manual process into an automated, insight-driven analysis model.
AI Log Alerts Summarization provides an immediate “why” for remediation
When a system alert is triggered on log data, the race to identify the root cause begins, often forcing engineers to wade through reams of log data under intense time pressure. AI Log Alerts Summarization from New Relic changes that dynamic by analyzing the related logs, highlighting dominant error patterns, and generating an actionable hypothesis for resolution.
Instead of simply notifying teams that a problem exists, AI Log Alerts Summarization provides the “why” behind the alert. Engineers receive a structured summary of the issue directly within their workflow, which shortens MTTR and reduces the stress of incident response. By shifting the analytical burden from humans to AI, teams can focus on executing fixes and maintaining system stability, rather than piecing together scattered log data.
“In distributed systems, the volume and velocity of log data can quickly overwhelm even the most experienced engineering teams,” said IDC Group Vice President Stephen Elliot. “AI-driven solutions that can automatically surface the root cause behind alerts and transform raw logs into actionable insights are critical for reducing downtime, accelerating incident response, and enabling teams to focus on higher-value work.”
Delivering actionable insights with Scheduled Search
Manual log queries consume valuable time and resources, often forcing engineers to chase repetitive reports instead of focusing on higher-value work. With Scheduled Search, New Relic eliminates that burden by automating query executions and proactively delivering critical insights when and where they’re needed. This automation not only saves time, but also ensures teams never miss key signals hidden in their log data.
Unlike competitors that provide static PDF dashboards, Scheduled Search delivers actionable reports directly via email or Slack. These reports go beyond surface-level visibility to include clickable insights that let operators take immediate action, reducing delays and improving efficiency. Scheduled Search equips teams to identify issues sooner, make faster decisions, and avoid costly downtime.
Fine Grain Access Control enhances compliance and security
New Relic’s Fine Grain Access Control for Logs gives organizations precise, policy-driven control over who can access specific log data without forcing them to compromise on observability. Security and compliance teams can enforce granular permissions that align with organizational policies, while engineers retain the ability to troubleshoot effectively using the full power of logs-in-context.