Operations | Monitoring | ITSM | DevOps | Cloud

Continuing my adventure to have Monitive being run by AI Agents, I had some brainstorming sessions with... well... AI. I was impressed about the maturity of the discussions one can have with ChatGPT 5.1 Pro and Claude Code Opus 4.5.

Your app feels slow, users complain… but your dashboard says response time is totally fine! But the real pain is revealed when looking at percentiles. In this post, we’ll break down why the 95th percentile response time metric is useful, plus how to work with it in practice. ‍

As the year winds down, I find that most cybersecurity and compliance teams are focused on closing projects, hitting targets, and maybe even planning a well-earned break. But regulators? They don’t take holidays. FCA, PRA, GDPR – they remain vigilant, and so should you. For IT leaders, this season often feels like walking a tightrope: balancing operational demands with the relentless need for compliance.

Today’s engineering organizations are built around service ownership. Service owners are accountable for keeping their services reliable, performant, and ready to scale. But no service operates in isolation; every team depends on others, and those dependencies form a complex web that can be hard to see, let alone understand. To truly deliver reliable systems, you need visibility not only into how your own service performs, but also how it affects others.

Modern business functions are based on the promise of smooth and seamless experience, without the need for downtime or long waits for backend processes to finish. For such digital operations, timely execution of business processes—like financial closings, order fulfilment, report generation—is non-negotiable.

AI coding assistants are quickly becoming a core part of software engineering workflows, helping developers write, refactor, and review code faster. But without effective monitoring, it can be difficult to know whether these tools are performing reliably and proving useful to engineers. As organizations scale their use of tools like Claude Code, key questions emerge.

When debugging production issues, investigating security incidents, or analyzing network traffic, engineers and analysts need not only to find the right logs but to make sense of all the dense, unstructured data generated by different systems. Logs rarely ship neatly laid out in a way that facilitates filtering, faceting, or graphing for every possible scenario. As a result, teams often find themselves writing regular expressions or custom parsers on the fly, which can be error-prone and time-consuming.

We’re excited to announce a set of updates across the entire VictoriaMetrics open source products suite — including VictoriaMetrics, VictoriaLogs, VictoriaTraces, the VictoriaMetrics Kubernetes Operator. These improvements bring better performance, stronger security, enhanced metadata visibility, and a smoother experience when running observability at scale.

The observability market has moved beyond manual threshold-setting. Modern platforms use statistical algorithms, machine learning, and causal AI to detect anomalies automatically. Some work immediately after deployment. Others train on your data for better accuracy. Each approach has technical trade-offs worth understanding. This guide compares how nine monitoring solutions handle automated anomaly detection and root cause analysis.