Operations | Monitoring | ITSM | DevOps | Cloud

Technical debt refers to the future costs and limitations incurred when organizations opt for short-term solutions over robust, long-term scalable architectures. For the middle mile, technical debt often manifests as equipment or network designs that restrict long-term flexibility, scalability, or interoperability.

We are witnessing a fundamental transformation in how software is built. The industry has moved beyond the experimental phase of Machine Learning Operations and entered a complex new reality: the era of the AI Software Supply Chain. The adoption metrics confirm this shift is irreversible. Google reports that 90% of tech workers are now using AI as part of their daily work. Similarly, McKinsey data reveals that 88% of organizations use AI in at least one business function.

Our December Harness AI updates focus specifically on enhancements to Harness AI governance. You can review our Harness AI November updates here.

Note: this post was co-authored by Nicholas Borg, 3CX Product Manager. 3CX provides a robust, flexible IP PBX platform used by organizations of all sizes to power their contact centers. It offers detailed call activity, agent performance metrics, and operational insights — all of which become even more powerful when visualized.

Even with a powerful database or visualization layer, performance can suffer if queries aren’t optimized or system settings aren’t tuned. The new Mimir Query Engine in Grafana Cloud improves query efficiency, but there are still best practices you can follow to keep dashboards fast and responsive—whether your data source is hosted in Grafana Cloud or running on-premises.

Comprehensive observability is critical for running performant, reliable, and secure serverless workloads. However, configuring and maintaining that visibility across hundreds or thousands of serverless functions can be difficult to scale and sustain. Developers across teams often manage serverless functions using different infrastructure as code (IaC) frameworks, as well as different review, deployment, and update processes.

Prior to working for a software company, I spent most of my career working for financial organizations. I have lots of friends who still do. Talking with one the other day, the question came up, what keeps you up at night? Her one word response was a little surprising: Fraud. Understand, she’s in charge of managing data at a bank. You’d expect maybe uptime, performance, high availability, any of the standard data management worries. Instead, it’s fraud.

As we step into a new year, one truth stands firm in financial services: resilience isn’t optional – it’s expected. Markets fluctuate, regulations evolve, and technology accelerates. Amid this complexity, IT leaders carry the responsibility of ensuring that operations don’t just survive disruption, they thrive through it.

It's 2026. Your New Year's resolution was to finally migrate to OpenTelemetry. But you're staring at dozens of dashboards that depend on your current data format, and that migration deadline is looming... Sound familiar? If you're an SRE or Platform Engineer facing a top-down OTel mandate, you're not alone. The challenge isn't just about adopting a new standard—it's about doing so without disrupting the observability systems your team depends on every day.

TLDR: AI coding assistants have sped up code delivery, but created a validation gap. Historic telemetry and static analysis cannot predict the behavior of unfamiliar, high-volume code. Lightrun’s Runtime Context MCP closes that gap, allowing AI assistants to verify behavior before it breaks, and resolve issues in real time.