Operations | Monitoring | ITSM | DevOps | Cloud

A common question and concern I hear about AI agents has to do with accuracy. This makes sense, I think. We’re accustomed to think of software systems as entirely deterministic—unpredictable results are a sign of bugs or logic errors.

Aerospace systems operate in one of the most unforgiving environments imaginable. Each flight test, orbital maneuver, or satellite transmission subjects avionics, propulsion systems, sensors, and telemetry hardware to extreme conditions. Even a minor failure can cascade into grounded aircraft, interrupted communications, or compromised missions.

Discover how Redgate Test Data Manager’s new AI features deliver fast, compliant, production-like test data - balancing realism, speed, and security. In regulated industries like finance, healthcare, and insurance, test data management (TDM) can be quite challenging when it comes to compliance.

Remember how I said that blog was going to be my last entry on the topic of "APM vs Observability?" Well, it turns out I had a little more to say. I'd like to spend a few moments talking about the future of APM and Observability. I think it comes down to two major initiatives: AI and Open Telemetry. (NOTE: in this section, I'm using the word "observability" to refer to the discipline of monitoring and observability as a whole, rather than any specific tool, technique, or vendor-based solution.)

At Progress, we continue to develop and enhance the Progress Flowmon product family. The latest update brings the core Flowmon product to release 13.0, and it includes remarkable performance improvements, strengthened security and expanded protocol support. Full details of what’s new and improved in the latest release are available on the Flowmon product page. In this blog, we’re excited to highlight the newest features and improvements to the Flowmon solution.

Sylva 1.5 becomes the first release to include Kubernetes 1.32, bringing the latest open source cloud-native capabilities to the European telecommunications industry With the launch of Sylva 1.5, Canonical Kubernetes is now officially part of the project’s reference architecture. This follows its earlier availability as a technology preview in Sylva 1.4.

A fundamental shift in software development is already here. Gartner predicts that by 2028, 75% of enterprise software engineers will use AI code assistants – a massive leap from less than 10% in early 2023. While this AI-driven speed creates a competitive advantage, it also opens a dangerous new front in the battle for software supply chain security.

When we first introduced the JFrog AI Catalog, it was our mission to provide the industry with a single system of record for governing the complex landscape of internal, open-source, and external commercial AI models. This foundational step was critical for enterprises to move from uncontrolled innovation to delivering AI with trust and confidence. However, the AI landscape is ever-evolving. The challenge for today’s enterprise is already evolving beyond simply managing a library of known models.

When you run Kubernetes at scale, one of the first challenges is understanding what the cluster is actually doing. Workloads shift around, pods restart for normal reasons, and traffic doesn't always follow the patterns you expect. Having clear signals makes day-to-day operations much easier. That's where kube-prometheus-stack helps. It brings Prometheus, Grafana, Alertmanager, and supporting components together as a single package.

In October, technologists from across the financial industry gathered in New York for OSFF 2025 where the general theme was clear: open collaboration has moved from promises to proof. Projects like Fluxnova and OpenGris showed how institutions can build shared, production-grade infrastructure. The Common Cloud Controls and AI Governance Framework demonstrated that regulatory assurance can be achieved collaboratively, not competitively.