Operations | Monitoring | ITSM | DevOps | Cloud

AI model drift is when an AI system's performance and accuracy degrades over time because the data, user behavior, or business environment has changed since the model was trained or evaluated. Even if latency, uptime, and infrastructure metrics remain healthy, model quality can quietly decline, leading to less accurate predictions, inconsistent responses, and reduced user trust.

Every team with more than a few data connections has had this moment: someone opens the connections list, points at one, and asks “what is this for?” The answer lives in a former teammate’s head or in a Slack thread. And cleaning up the wrong connection can break cost ingestion. Now each connection can carry a note that explains why it exists, and anyone who opens the connection sees it.

AI agent cost in 2026 is mostly a consumption bill, not a subscription. Running an agent costs anywhere from fractions of a cent for a simple routed task to $5 or more for a complex multi-step job, because one request can trigger 3 to 10 model calls behind the scenes. Average production deployments land between $3,200 and $13,000 per month in operational spend. Here is where that money actually goes.

You shouldn’t have to hunt through a cluttered dashboard to understand where your code is deployed. The Deployments page is where teams turn for a quick answer: what is in test, what is in staging, what reached production, and what needs attention right now. The older page made that harder than it needed to be. Our new Pipelines Deployments page makes it easier to scan, filter, and act on.

Traditional FinOps focuses on cutting overspend, but the real opportunity lies in maximizing savings you're missing. This paradigm shift reframes cloud cost management as a proactive savings optimization strategy rather than reactive spend control, helping organizations unlock hidden cost efficiency through governance, automation, and continuous optimization practices. Cloud spend is up 40% year-over-year. Your CFO wants answers.

Manual intake eats the first 10 to 30 minutes of an incident. Harness AI SRE runs it as automation and gives those minutes back to your responders.

Serverless platforms like AWS Lambda, Google Cloud Run, and Azure Container Apps let teams run applications without managing infrastructure. However, getting full visibility into those workloads has traditionally required a lot of manual setup. A single team may deploy serverless applications across multiple clouds by using tools such as Terraform, AWS SAM, AWS CDK, and the Serverless Framework. Each of these platforms, runtimes, and deployment tools requires its own instrumentation steps.

Different teams ship on different clouds, each setup grows its own pipeline and runbook, and the fragmentation surfaces later as missed deadlines and extended audits. By then the operating-model debt has been compounding for months.

Managing AI infrastructure in a traditional enterprise data center comes down to validating that sufficient capacity exists before hardware arrives, then maintaining accurate infrastructure data to support planning, deployment, troubleshooting, and ongoing operations. This is because AI has changed what enterprise data centers were built to handle.

Field service managers need visibility into more than just whether a technician has arrived. They need to know when work begins, when it’s completed, and when the technician is heading to the next job. Without a simple, consistent way to communicate these milestones, dispatchers and supervisors are left making phone calls and sending text messages just to find out what’s happening. Most of the time, nothing is wrong.