Operations | Monitoring | ITSM | DevOps | Cloud

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

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.

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.

As our applications grow from simple side projects into complex distributed systems with many users, the “old way” of console.log debugging isn’t going to hold up. To build truly observable systems, we have to transition from simple text logs to structured, queryable, trace-connected events.

In 2025, DevOps teams faced a pivotal moment. The era of treating security as an afterthought was over. Practically overnight, airtight protection became a non-negotiable requirement across every layer of the technology stack, whether on prem, in the cloud, or at the network’s edge. For many teams, this wasn’t just a technical hurdle; it was a daily source of stress.

InfluxDB is a widely used time-series database designed for storing and querying metrics, events, and telemetry data. It’s commonly used for infrastructure monitoring, application instrumentation, and IoT-style workloads where time-based data is central. In many environments, InfluxDB already exists as part of the monitoring or data collection pipeline, and the primary need is simply to visualize that data effectively.

dbt is one of the most popular solutions for data transformations and modeling. Many commercial data pipelines rely on dozens, or even hundreds, of individual dbt jobs. Data engineers, data platform engineers, and analytics engineers who own these pipelines need to maintain a testing framework to prevent mistakes in data processing that can compromise analysis.

Fine-tuning Large Language Models (LLMs) on private, domain-specific data can unlock significant value for your specific use case. When done correctly, you can create AI apps that understand your organization’s unique context. These apps can speak your brand’s voice and deliver remarkably accurate results that general models cannot match. However, finetuning is not always the right solution. Many teams rush into this complex technique without exploring simpler alternatives first.

To mark the launch, we’re publishing Agentic AI Essentials, a four-part series to help organizations navigate the reality of agentic AI adoption. Across the series, we’ll look at the questions that matter most: what’s real versus hype, how to avoid adoption pitfalls, how to measure ROI, and how roles will evolve once agents are onboarded. Here’s a sneak peek at what’s in store.

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.