Operations | Monitoring | ITSM | DevOps | Cloud

Gartner predicts that by 2029, agentic AI will autonomously resolve 80% of common customer service issues, reducing operational costs by 30%. That shift points toward a zero-friction workplace, where employees do not have to navigate support just to get back to work.

Netdata ships with hundreds of stock alerts. They cover a wide range of infrastructure conditions and they’re designed with sensible defaults. But “sensible defaults” and “correct for your environment” are not the same thing. A CPU threshold that’s perfectly reasonable for a build server might generate constant noise on a machine running batch jobs.

Many organizations deploying AI are learning similar lessons right now: the challenge isn’t this or that AI model, it’s the data. According to Gartner, 60% of AI projects will be abandoned by organizations because of failures to support these projects with AI-ready data. Also, 63% of organizations either lack or aren’t sure they have the right data management practices to get there.

If you've ever run an ALTER TABLE on a busy MySQL table in production, you know the feeling. The change is small. The risk isn't. Long-running table locks, queued writes, application timeouts, replication lag, a five-minute migration that turns into a half-hour incident review. We're shipping an integration that takes that anxiety out of the loop. Harness Database DevOps now supports Percona Toolkit for MySQL as part of Liquibase-based schema management.

Most development teams today build everything around Git, and deploy with GitOps principles. Code sits in version controlled environments, changes go through PRs, and deployments are handled through modern CI/CD. That part is pretty standard at this point, especially when using a modern DevOps platform like Harness.

Developers building agentic AI on Google Cloud have powerful infrastructure at their fingertips: Gemini 3 for reasoning, Google’s Agent Development Kit (ADK) for orchestration, and a rapidly expanding ecosystem of Model Context Protocol (MCP) servers that connect agents to data and tools. So why are so many teams still waiting weeks to ship their first agent to production?

The most common Apache ActiveMQ high availability mistake is not a configuration error; it is a false assumption. Teams deploy two broker instances, point clients at both with a comma-separated URL, and label the topology "HA." Then the primary crashes, the secondary does not have the message state, and clients start throwing exceptions while the ops team scrambles.

Sometimes, we need to do some spring cleaning when it comes to our devices, as over time, leftover files can be left on our devices, or junk files could be slowing down our computer and taking up our local storage. While you can clean up your device yourself, this can be time-consuming and risky, as you could delete an important file essential to keeping your device running.

AI has since become an integral part of our lives, whether it’s for work or personal use; we all use AI in some form or another. However, deciding which is the best AI depends on how you want to use it. Whether it's for general questions, coding, deep research, or image creation, we’re lucky enough that there is an AI model available to help you out.

Health check endpoints generate thousands of identical, useless spans per day. Here are two production-ready approaches to filter them from your Python OTel traces — and the correctness trap most implementations miss. Prathamesh works as an evangelist at Last9, runs SRE stories - where SRE and DevOps folks share their stories, and maintains o11y.wiki - a glossary of all terms related to observability.