Logz.io Releases Alert Recommendations, Enlisting AI to Accelerate and Reduce MTTR

Logz.io, provider of the industry’s most efficient, simple-to-use observability platform, based on open source, announced the general availability (GA) of its new Alert Recommendations capability in the Logz.io Open 360 platform.

Based on patent-pending technology, the Alert Recommendations feature automates knowledge creation for organizations by capturing the investigative approach of highly trained engineers receiving alerts. Alert Recommendations specifically employs AI to model the steps of platform users as they carry out their work, using that input to recommend alert response steps during subsequent investigations. Based on a supervised machine learning model, this new capability is now available to Logz.io Open 360 users in GA after building significant intelligence from customers in early access.

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Previously, the industry trend was for engineers to document and share response steps in runbooks to speed remediation. But that process has proven to be ineffective, especially as teams grow and top engineering talent becomes harder to come by. Maintaining runbooks typically requires significant manual work, and the speed of modern production environments makes it increasingly difficult for this type of guide to be adequately maintained and enforced.

*Algorithm Adapted to the Reality of Engineers’ Investigative Path*
With Alert Recommendations, the runbook concept is being adapted to address the dynamic nature of today’s environments, creating automation using supervised machine learning. Now, every time there is a new investigation, Logz.io Open 360 automatically monitors the investigative path of different team members and identifies which steps resulted in the fastest time to resolution. The methods with the best Mean Time to Recovery (MTTR) results are identified, and an automated path is then created for subsequent alerts, eliminating the need for laborious documentation or inefficient actions.

With engineering teams that vary in their degree of expertise, Alert Recommendations also creates a critical knowledge base and automated path that eliminates multiple steps, condensing the timeframe from when the alert is first received to the first investigative action. Valuable engineering resources are optimized while MTTR is reduced.

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*MTTR is Increasing with Wide Adoption of Kubernetes*
According to the 2023 Logz.io DevOps Pulse Survey, MTTR has steadily increased within organizations—after a 10% increase from last year, 75% of respondents now say it’s taking them hours to resolve production issues. Only 14% of respondents are satisfied with their current MTTR, indicating a strong need for improvement. And within the rapidly growing microservices and Kubernetes segment, over 41% of survey respondents cited monitoring and observability of Kubernetes as a primary challenge of running it in production, a 10% increase over last year.

The survey finds the biggest issue facing today’s monitoring and observability teams is that MTTR continues to increase, taking more than a few hours for over 73% of respondents, up noticeably from 64% in 2022 and dramatically from only 47% in 2021. The Alert Recommendation capability is coming at an important time in the evolution of AIOps.

“The Alert Recommendations capability represents the future of AIOps, making it possible for users and platforms to automate even more of the work that currently consumes so much time and so many resources,” said Asaf Yigal, CTO and co-founder of Logz.io. “Organizations with limited human resources need efficient and reliable tools to translate their observability data into simpler, more actionable insights. With this capability, the Logz.io Open 360 platform harnesses the actions of highly skilled engineers while reducing remediation time.”

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