Operations | Monitoring | ITSM | DevOps | Cloud

Learn how private connectivity, regional hubs, and AI-driven automation are defining the next evolution of enterprise networking in 2026. 2026 is shaping up to be a big year for networking. We’re moving past the ideas of being simply connected – now, networks are becoming intelligent. As we see our customers lean into AI, multicloud, and automation in every corner of their operations, the way they connect everything is changing just as fast.

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.

As organizations accelerate their AI-driven development, the need for trustworthy and transparent experimentation is greater than ever. Warehouse Native Experimentation keeps analysis where the data already lives, enabling teams to validate features with metrics and reliable SQL logic. The result is faster iteration with less risk, and decisions rooted in the same source of truth the business already trusts.

The end of the year brings pressure. (Oh, we know!) Customer demand spikes, response expectations stay high, and engineering teams are juggling production issues, releases, and time off. For many teams, this is when on-call becomes chaotic: schedules break, notifications hit at the wrong time, and coverage gaps appear exactly when you can’t afford them. ‍ ilert's Holidays and Support hours features were built to fix that.

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.

Icinga Director is a powerful tool that greatly simplifies the configuration, management, and deployment of monitoring objects in Icinga. It provides a user-friendly interface and automation features that make complex setups easier to maintain. Occasionally, though, you may unintentionally introduce a cyclic inheritance while configuring templates. A typical case occurs when a template imports another template that eventually imports the original one again.

Back in September, we unveiled Sumo Logic Dojo AI, our agentic AI platform built to power intelligent security operations and incident response. With that launch, we introduced Mobot, our conversational interface, as well as our first agents designed to help automate routine tasks, streamline investigations, and give security teams the freedom and ability to focus on analyzing the highest value security issues facing their organization. Today, we’re excited to share the latest additions to Dojo AI.