Operations | Monitoring | ITSM | DevOps | Cloud

As enterprises face increasing pressure to manage vast and complex IT environments, the demand for faster and more efficient IT management is rising. Traditional operating methods are proving insufficient, making the adoption of Agentic AI essential for organizations aiming to achieve truly autonomous IT operations. This innovative technology enhances decision-making and enables businesses to remain agile in a rapidly evolving digital landscape.

Connecting day-to-day development work to real user outcomes can be challenging. As a result, engineers and product teams often struggle to effectively prioritize projects together. While the goal of improving user experience (UX) is the same, each team relies heavily on different—and often siloed—forms of monitoring to understand their app, creating a disconnect in metrics and visualizations that can be hard to communicate.

The performance of your Kubernetes operators often influences the behavior of the applications they manage. Operators automate the day-to-day management of your applications by executing critical activities, which may include scaling replicas, performing upgrades, and recovering from failures. For example, a PostgreSQL operator can ensure that standby servers are always deployed, that the database’s failover is correctly configured, and that data is backed up on schedule.

Security teams face a critical question: “What logs should we collect, and what detections should we enable to protect against threats targeting our industry?” For a bank in the northeast, this isn’t academic. Threat groups like FIN7, Lazarus Group, and Carbanak specifically target financial institutions with sophisticated attacks ranging from SWIFT compromise to ransomware.

If there is one truth I have learned working with financial services firms in 2025, it is this: AI is no longer optional, it is operational. From risk modeling to customer experience, algorithmic trading to automated compliance checks, AI is now embedded into the fabric of modern finance. But there is a second, quieter truth. AI only creates value when it is used responsibly, measurably, and at scale.

If data centres were a country, they’d rank fifth in electricity consumption by 2026. Over the past few years, the resulting carbon footprint of the technology industry has sparked the fast-growing green software movement, led by the Green Software Foundation. How can we continue to innovate software in a way that also minimises its impact on the environment? This has been a fascinating problem I’ve been exploring for a few years now.

VictoriaMetrics has achieved Red Hat OpenShift Certification, awarded to Red Hat partners who meet requirements for delivering a scalable, supported, and secure operator designed for enterprise cloud deployments. VictoriaMetrics available on the Red Hat OpenShift OperatorHub The program certified VictoriaMetrics as a solution that allows for portability and operational efficiency across hybrid and multi-cloud environments.

AI agents are fundamentally different beasts to monitor compared to traditional applications. A single user request can trigger a cascade of 10+ internal operations: sub-agent transfers, tool executions, LLM calls, API requests, each with unpredictable latency and failure modes. When something goes wrong (and with LLMs, things go wrong in creative ways), you need to see the entire execution flow to debug effectively.

Broken checkouts lead to lost transactions, drain revenue, undermine customer trust, and damage brand credibility. Unfortunately, most companies don't realize their checkout is failing until sales drop or customers start complaining. According to statistics, technical issues cause checkout abandonment in at least 17% of cases. This means nearly one-fifth of lost conversions are preventable. For any online business, even a small checkout failure can result in significant revenue loss.

You’ve optimized your code, implemented caching strategies, and configured your CDN perfectly. Your analytics dashboard shows respectable load times, and your development team reports everything is running smoothly. Yet, conversion rates remain stagnant, bounce rates climb during peak hours, and your competitors consistently outperform you in user experience metrics. What’s missing?