Operations | Monitoring | ITSM | DevOps | Cloud

What's New in InfluxDB 3.11: A Significant Performance Upgrade for Complex Time Series Workloads

Summary InfluxDB 3.11 delivers major performance and data management updates for growing time series workloads, with significantly faster queries on recent data, expanded support for wide and ultra-sparse schemas, and a more predictable resource profile under load. InfluxDB 3 Enterprise also adds backup and restore, bulk Parquet import, row-level deletes, and a built-in Explorer UI.

GitLens 18 Turns the Commit Graph Into an Agent Command Center

Five coding agents sounds like leverage right up until a developer is the one keeping track of all five: one fixing a bug, one building a feature, one refactoring, and two waiting on input at the same time. AI did not create that problem. It exposed a workflow problem that was always going to surface once parallel development became normal instead of occasional.

Introducing usage-based billing in MSP Central!

Billing has always been one of the parts of running an MSP that doesn't scale on its own. More endpoints, more tickets, and more monitors under management all mean more usage to track—and for most MSPs, that usage still gets tallied by hand before an invoice can go out. Not anymore. We're rolling out the MSP Central billing module, powered by our integration with Zoho Billing—and it's live with usage-based billing from day one.

What's New in InfluxDB 3: 5 New Processing Engine Plugins

Summary The five most recent plugins from the InfluxDB team are live: Sagemaker, value counter, Chronos forecasting, simple data replicator, and a stock portfolio tracker. Table of Contents The InfluxDB team has released five new Processing Engine plugins. They range from making it easy to call a hosted ML model to pulling in stock market data in real-time. Every one of them can be activated with a few terminal commands.

Introducing Harness AgentTrace: An Observability and Guardrail Framework for AI Agents | Harness Blog

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.

Announcing the Harness CLI: Built for Humans and Agents | Harness Blog

---‍Key Takeaway: Today, we're launching the public beta of the Harness CLI: the single, officially supported command-line tool for the entire Harness platform. It replaces the older per-module CLIs with one binary, one grammar, and one auth flow across pipelines, CD, code, artifacts, IaCM, feature flags, governance, and audit. Designed for secure DevSecOps and enables terminal workflows for developers and deterministic execution for AI agents. ---

The Advanced Pipeline Editor Is Here: One View, Every Pipeline

The Advanced Pipeline Editor is now live for all paid Bindplane plans. It's a rebuilt configuration editing experience that puts your whole config in a single interactive graph: every source, processor, router, and destination, across logs, metrics, and traces, in one view you can search, pan, zoom, and edit directly. If you've ever bounced between pipeline tabs trying to figure out where a processor sits in a config with a dozen sources and three destinations, this release is for you.

Announcing vmestimator: Real-time Cardinality Estimations for VictoriaMetrics and Prometheus

Cardinality problems usually begin with a small change that looks harmless: you add a label, and suddenly one metric turns into thousands of unique series. Cardinality explosions are often caught only after performance degrades. And at that point, your observability stack may be degraded and painful to troubleshoot. vmestimator is a new project specifically designed to follow cardinality trends in real time and send you alerts before they turn into a real problem.

Introducing AI-Powered Incident Correlation & Root Cause Detection

An API latency spike hits your checkout service, and within ninety seconds your on-call phone won't stop buzzing. A CPU threshold breaches. A database connection pool exhausts. A pod restarts. An error rate crosses 5% on a downstream service. Six engineers get paged inside four minutes. Forty alerts. Seven services. One incident. Every monitoring tool in the stack is doing exactly what it was configured to do, telling you that something is wrong.