How Rootly AI Predicts and Prevents Reliability Regressions

In modern software development, a "reliability regression" occurs when a new change degrades system performance or stability. These regressions are more than just technical glitches; they carry high costs in the form of service downtime, customer dissatisfaction, and engineering toil. For Global 2000 companies, system outages result in an estimated $400 billion in annual losses [1]. To combat this, teams need to shift from a reactive to a proactive reliability posture. Rootly AI is a proactive solution designed to predict and prevent these issues before they impact users.

What Are Reliability Regressions and Why Do They Happen?

A reliability regression is a state where a system's stability or performance worsens following a change, such as a code deployment or a configuration update. Predicting these regressions is difficult because modern systems are complex and dynamic, often involving interconnected microservices, third-party APIs, and even AI agents operating in distributed environments.

Common causes of reliability regressions include:

• New code deployments with unforeseen side effects.
• Infrastructure changes in complex cloud environments.
• Configuration drift that happens gradually over time.
• Failures in third-party dependencies that your services rely on.

The severe business impact of these issues highlights the growing need for advanced tools to manage reliability. Solutions like Rootly provide the necessary intelligence to navigate this complexity.

How Rootly’s AI Predicts and Prevents Reliability Regressions

So, how can Rootly’s AI predict and prevent reliability regressions? It combines predictive analytics, real-time monitoring, and automated response to give teams an advantage over potential failures.

Proactive Risk Assessment with Predictive Analytics

Rootly AI analyzes historical data from past incidents, changes, and system metrics to identify patterns that often precede failures. This allows the platform to provide AI-suggested risk information, enabling teams to evaluate the potential impact of a change without extensive manual effort [2]. This proactive risk assessment is consistent with modern defect prediction models that aim to manage software health by forecasting when issues might arise based on performance parameters [3]. By understanding the risk upfront, teams can make smarter, data-driven decisions about their deployments.

Real-Time Anomaly Detection

Traditional monitoring often relies on static, predefined thresholds that can miss subtle but significant deviations. Rootly AI takes a more advanced approach by establishing a dynamic baseline of your system's normal behavior. Using machine learning, it detects subtle anomalies that can signal an emerging regression. This proactive strategy helps teams find and fix problems hours or even days before they become user-impacting incidents. You can learn how Rootly AI uses anomaly detection to forecast downtime and get ahead of potential issues.

Automated Mitigation and Response Workflows

When Rootly AI detects a high-risk change or an active anomaly, it doesn't just send an alert; it can automatically trigger predefined workflows to initiate a response. These automated actions can include:

• Creating a new incident directly in Rootly.
• Notifying the correct on-call engineers via Slack, SMS, or other channels.
• Populating the incident with relevant data and context from various monitoring tools.
• Suggesting or initiating rollback procedures for the high-risk change.

This automation ensures a fast, consistent, and well-documented response that follows a clear incident management lifecycle.

How Rootly Supports Data-Driven Reliability Decisions

True reliability improvement isn't just about preventing incidents; it requires good data and the right tools for analysis. This is how Rootly supports data-driven reliability decisions.

Centralized Data for Deeper Insights

Rootly acts as a single source of truth, capturing comprehensive data for every incident and regression in one place. Its powerful analytics dashboards help you visualize trends, identify repeat failures, and track key metrics like Mean Time to Recovery (MTTR). By centralizing this data, Rootly helps its own teams deploy code 10-20 times daily and has reduced its MTTR by 50% [4]. This centralized, data-rich environment helps your teams uncover systemic weaknesses and prioritize long-term improvements that strengthen your entire system.

Automated Post-Incident Analysis for Continuous Learning

This is how Rootly uses AI for continuous reliability improvement: by automating the most time-consuming parts of the post-incident process. This frees up engineers to focus on learning and prevention rather than on manual report writing.

Key features that facilitate this include:

• Incident Summarization: Generates on-demand reports of an incident's status and key events, keeping all stakeholders informed.
• Mitigation and Resolution Summary: Automatically documents the steps taken to fix an issue, preserving valuable context for future analysis.
• "Ask Rootly AI": Lets users ask questions in plain English to quickly find information and understand the nuances of any incident.

These features reduce toil and create a powerful feedback loop for continuous improvement. For more details on these capabilities, you can explore the AI & Intelligence Overview.

Creating a Culture of Continuous Reliability Improvement

Adopting AI for reliability isn't about replacing engineers—it's about empowering them. Rootly AI is designed to work alongside your teams to create a stronger, more resilient organization.

Augmenting Engineering Expertise

Rootly AI is a human-in-the-loop system that enhances your team's existing expertise. Site reliability engineers (SREs) play a vital role in validating AI-generated outputs. As reliability engineering expands to include the management of AI agents, ensuring human oversight is critical for maintaining determinism and control in complex workflows [5]. The Rootly AI Editor enables users to review, edit, and approve all AI-generated content, ensuring every summary and report is accurate and context-aware. This collaboration between human experts and AI ensures that insights are not just fast but also trustworthy.

From Firefighting to Strategic Prevention

By predicting and preventing regressions, Rootly AI fundamentally changes an organization's approach to reliability. This shift reduces the frequency of disruptive, all-hands-on-deck incidents, which helps alleviate engineer burnout and builds a more sustainable work environment. Instead of constantly putting out fires, your teams can dedicate their time and talent to innovation and building value. Embracing an AI-driven approach is a key step for any organization looking to build a more reliable and resilient future.

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