Operations | Monitoring | ITSM | DevOps | Cloud

2025 was a stark reminder of why reliability is so critical in the tech sector. The year wrapped up with multiple high-profile outages across several major cloud providers, costing companies around the world billions of dollars. Building resilient systems has never been more of a priority, especially as we move into the era of agentic AI.

Imagine this. A product team rolls out a change to improve reliability. The deployment succeeds. Traffic grows. Weeks later, cloud costs increase, and the finance team asks what changed. No one can point to a single decision or owner. This situation is common in cloud environments. Infrastructure scales automatically, and costs are shaped by technical choices made across engineering, data, and product teams. Most organizations review cloud spending after it has already occurred. Ownership is unclear.

When something breaks in a modern digital service, customers feel it instantly. Pages stall, requests time out, and carts are abandoned, while frustration grows long before a root cause is identified. What the world never sees is the engineering effort required to keep these systems healthy in the first place. Site Reliability Engineers (SREs) carry that responsibility every day.

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.

A practical, real-world comparison of ITSM tools that actually work for small and growing IT teams.

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.