Operations | Monitoring | ITSM | DevOps | Cloud

When an alert lands, does your team already see the full story, or does the work start with pulling scattered data together from one tool after another? For many organizations it's the second one, where the incident itself takes a backseat while analysts hunt across dashboards. The evidence is right there, scattered across platforms that don't share context. Security observability exists to close that gap.

On October 27, 2023, the Apache Software Foundation published CVE-2023-46604, a CVSS 10.0 remote code execution vulnerability in Apache ActiveMQ that allowed unauthenticated attackers to execute arbitrary code by exploiting the OpenWire protocol's ClassInfo deserialization.

Most ActiveMQ performance benchmarks are wrong, not slightly off, but fundamentally invalid for capacity planning. Performance benchmarking done incorrectly is worse than not benchmarking at all. A number that looks like a throughput measurement but was collected without JVM warmup, without latency percentiles, with the load generator co-located on the broker host, and while producer.

Last9 and Altinity now run observability entirely in your own cloud, metrics, logs, traces, and profiles on an open-source ClickHouse stack, priced on capacity instead of ingestion, with Altinity operating the database so your team doesn't have to. Last9 is an observability platform built for high-cardinality telemetry. It unifies logs, metrics, and traces with native OpenTelemetry and Prometheus support, real-time alerting, and long-term retention.

Summary InfluxDB plays a critical role in LeoLabs’ infrastructure, enabling a lean team to operate with confidence that potential issues will be detected and surfaced in real-time. By offloading the complexity of managing time series data at scale, engineers are free to focus on higher-impact work (such as optimizing their radar network) rather than maintaining and troubleshooting database systems.

While most on-call schedules are built to represent regular rotations, often on a weekly basis, not all of your on-call needs require the same coverage every week. We’ve added Custom Shifts to the Shift-Based Schedules for maximum flexibility. Custom shifts are a feature of our new Shift-Based Schedules. With Custom Shifts, your team can cover ad hoc needs for special events, major deploys, Failure Fridays, gamedays, or whatever comes up that needs some extra coverage.

Ask any engineering leader how AI has changed their team, and you'll get an optimistic answer. Ask them to prove it with a number, and most go quiet. That gap is the story of 2026. Individual engineers are faster than they've ever been. Teams, in a lot of cases, aren't. We talked to our own engineering team, the people who've spent the last year building and shipping Upsun Dispatch and living through exactly this shift, and the same tension came up again and again.

Enterprise workflow automation can deliver faster resolution and ultimately more time back for IT teams. But not every workflow is ready to automate on day one, is it? This is where many automation programs stumble. Teams often start with signals like the most visible problem or the workflow that creates the most frustration. These are useful signals, but they’re not enough on their own. The best automation candidates are usually repeatable, measurable, and have clear inputs and outputs.

AI agents fail differently from the software we spent the last two decades learning to monitor. We hear some version of the same story from teams shipping agents to production: an agent starts producing wrong answers. Not obviously broken: confident, well-formatted, plausible wrong. The logs are clean, latency looks healthy, and error rates sit at zero. Nothing flags a problem. A user eventually does.

‍Teams building agents have converged on something that looks a lot like the software development lifecycle, but reshaped around a system whose output isn't deterministic: prototype an agent against a framework, evaluate it against a dataset of expected behavior, deploy it somewhere real, observe how it behaves against live traffic, and feed what you learn back into the next prototype. Call it the agent development lifecycle (Agent DLC).