Operations | Monitoring | ITSM | DevOps | Cloud

After months of development, testing, and community feedback, we’re excited to announce the general availability (GA) release of InfluxDB 3 Core and InfluxDB 3 Enterprise. This release brings us closer to our vision for InfluxDB: a time series database that helps developers solve the problem of collecting, analyzing, monitoring, and acting on data across sensors, networks, servers, and applications. We view time series as a way to analyze, monitor, and act on data through time.

At Google Cloud Next, one statement particularly caught the attention of innovators and cybersecurity professionals alike: Google’s introduction of Agent 2 Agent (A2A) marks a major evolution in AI architecture. It enables autonomous agents to collaborate across services, platforms, and domains—unlocking powerful use cases across virtually every industry.

Quantitative risk assessment offers an objective approach to risk analysis – but understanding the risk is only the first step. This article will break down how to interpret the results and translate those insights into meaningful decisions in a real-world environment.

APIs are the heart of most modern applications. Due to their simplicity and lightweight design, RESTful APIs are a popular choice for client-server communication in most applications. However, APIs can become limiting when fetching complex or related data. The front end may over-fetch or under-fetch the meaningful data. For example, different pages require different responses. RESTFul APIs require different field endpoints, each involving repetitive complex joining conditions.

If you got your hands on a high-performance sports car, would you dare to drive it without a dashboard showing speed, fuel levels, or engine health? Absolutely not! Just as a dashboard helps keep your car running at peak efficiency, an application monitoring tool serves as your app’s digital dashboard, ensuring high performance and preventing unexpected crashes.

What’s the cost of a slow network if you are working in financial circles? A one-second delay in trade execution can mean millions in lost revenue. A lag in payment processing? That’s frustrated customers raising thousands of support tickets that your team would struggle to handle and potential compliance fines. For CIOs, CTOs, and IT leaders in financial services, keeping networks up and running is a business imperative.

In the ever-evolving world of artificial intelligence, a new frontier is emerging—Agentic AI. This revolutionary concept goes beyond the traditional models of AI that we’ve grown accustomed to. Instead of simply following explicit instructions, agentic AI systems are designed to act autonomously, make decisions, and adapt dynamically. In other words, they can “think” independently to achieve specific goals.

Just a quick blog to let you know that eG Enterprise v7.5 has introduced support for further ticketing systems – including for popular ITSM solutions such as BigPanda and Freshservice.

In 2025, the average enterprise juggles over 150 SaaS applications, hybrid cloud infrastructures, and a workforce that expects seamless digital experiences—yet most CIOs still rely on monitoring strategies built for the data center era. The result? A $1.5 trillion annual hit to global GDP from downtime and performance lags, according to recent industry estimates. The problem isn’t the tools—it’s the thinking behind them.

AI agents are everywhere in the headlines—and yet no one seems to agree on what they actually are. Ask five companies what it means, and you’ll get five different answers: So yeah—no wonder people are confused. At the highest level, everyone agrees on this: AI agents are systems designed to act on behalf of a user. But that’s where the agreement ends. The big differences come down to how independent they are, how intelligent they really seem, and what kind of work they can do.