Operations | Monitoring | ITSM | DevOps | Cloud

Most engineering teams underestimate the time infrastructure demands from them. The hidden cost isn't in provisioning, it's in the accumulated friction of environment drift, manual handoffs, and repetitive infrastructure maintenance that quietly consumes hours your team should be spending on product.

Cloud hosting is not invisible. Every project deployed, every resource provisioned, every region selected carries a real energy cost, and that energy cost has a climate cost. At Upsun, we've known this for a while. What we're sharing today is where we stand, what we measured, and what we've committed to doing differently from 2026 onwards. Our ambition is calibrated to what we can credibly deliver, and we think being upfront about that matters more than overpromising.

11 takeaways from the Simple Talk podcast on security vs speed in databases: why teams misjudge risk, how AI amplifies bad habits, and what to do about it.

Single agents are a useful starting point for SRE workflows. They are not where the architecture should end. The first version is simple enough: connect an LLM to a few tools, give it a system prompt, and point it at your infrastructure. It can summarize an alert, pull logs, answer questions, and draft a useful next step. Then the workflow gets real. You add GitHub for runbooks, Kubernetes for cluster state, PagerDuty for incident context, Prometheus for metrics, and Mezmo for telemetry.

Last week, we introduced Agent Timeline, a powerful new observability experience purpose-built for debugging AI agent workflows in production. Agent Timeline uniquely connects AI-layer visibility to full-stack observability by organizing telemetry around an agentic conversation. A conversation contains one or more agent executions, each of which may contain LLM calls, tool invocations, handoffs, retries, human escalations, and downstream system calls.

The average person would need 180 million years to read everything published online in a single day. For organizations trying to track what people say about their brand, manual monitoring stopped being viable somewhere around 2015. AI-powered media monitoring tools now process this impossible volume automatically, detecting brand mentions, analyzing sentiment, and flagging potential crises before they spiral.

Last month, we introduced Agentic Pipelines, a new way to orchestrate AI agents to automatically, and routinely, handle the repetitive engineering chores so you can get back to solving the fun, cool problems. When we launched, Agentic Pipelines supported Atlassian’s developer AI agent, Rovo Dev. Today, we’re opening up Agentic Pipelines to even more teams: You can now run agentic steps in your pipeline with Claude as the provider.

The platform has been live for six months. Workflows are running, the virtual agent is fielding requests, and the vendor dashboard shows deflection numbers are going up. Then someone pulls the actual ticket volume report, and it looks almost identical to the one before the rollout. This comes up constantly in enterprise IT, and most teams respond the same way. They tell themselves the platform needs more automations, a wider user base, and another quarter to mature. Months pass.

Your dashboards are green. Your thresholds are calm. Then a cascade failure starts and you don't know until users flood your status page. Traditional monitoring is reactive by design. Anomaly detection in observability changes that equation entirely.

Author: Matthes Derdack Businesses rely on countless systems, applications, and services to operate without disruptions. Whether it is cloud infrastructure, manufacturing equipment, IoT devices, healthcare platforms, or enterprise applications, every second of downtime can impact revenue, customer trust, and operational efficiency.