Operations | Monitoring | ITSM | DevOps | Cloud

Have you tried using AI at work and felt it works well for small tasks, but not beyond that? It can handle simple things like creating a summary, writing a draft, or answering a question. This works because the task is clear. But most tasks are not that simple. They involve multiple steps. One step depends on another. Data comes from different systems, and some decisions need checks before moving ahead. This is where a single AI system starts to struggle.

OpenTelemetry made telemetry possible everywhere – turning observability pipelines into distributed production infrastructure. Distributed infrastructure requires a control plane for inventory, governance, and safe change. At 500 collectors across hybrid environments, operational overhead becomes a production risk. The moment telemetry pipelines become a distributed infrastructure, they inherit the operational problems of one.

When your infrastructure is spread across regions, data centers, branch offices, or edge locations, knowing where a node is physically located matters more than people usually admit. During an incident, “the node in the Singapore POP” communicates faster than a hostname. When you’re planning capacity, seeing geographic clustering tells you something that a flat list of nodes doesn’t.

On May 7th and 8th, 2026, Amazon Web Services (AWS) experienced an outage affecting Amazon Elastic Compute Cloud (EC2) in the dreaded US East 1 region. The original region of AWS located in Northern Virginia, us-east-1 or just “US East” as it is known, has been the subject of some of the internet’s most high profile and destructive outages and remains Amazon’s least reliable region.

Most IT teams do not suffer from a lack of data. They suffer from the amount of effort required to make sense of it. Every network device, application, cloud service, and infrastructure component generates a constant stream of machine output. Logs capture state changes, failures, retries, warnings, and thousands of other small signals about how systems behave. The problem is that raw logs are hard to use at operational speed.

Observability in large companies is rarely one-size-fits-all. The VictoriaMetrics topologies guide shows why different deployment patterns are needed as scale, isolation, and reliability requirements grow. Different workloads require different trade-offs: some need long retention for audits and trend analysis, while others need higher resolution for debugging. Business-critical systems also demand dependable alerting and high availability, often with several 9s of reliability.

Your Unreal game can ship with zero errors and still not feel great. Stutters during combat, a frame-rate cliff on the big boss, rubber-banding in multiplayer, none of it shows up as a crash and none of it shows up in Sentry, leaving you without any visibility into what your players are actually experiencing in the wild. Well, until now. Unreal Engine already gives you plenty of tools to measure game performance and collect runtime stats, but all that data stays on the dev’s machine.

In a recent webinar, The Journey to Production AI, Andre Elizondo walked through what separates a working agent demo from an agent worth trusting on a 2 a.m. page. Live polls during the session put numbers behind a pattern most platform teams already feel. ‍ ‍ Most teams are early. The ones who are further along did not get there by shipping a flashier demo. They got there by treating production AI as a platform problem.