Operations | Monitoring | ITSM | DevOps | Cloud

Automating root cause analysis is often regarded as the holy grail of IT operations. A solution capable of automatically identifying issues, resolutions and even prevention. Performed correctly, automated root cause analysis accelerates MTTI (Mean Time to Identify) and MTTR (Mean Time to Resolution). But for many platforms, this goal remains elusive: complexity, differences between deployments and different architectures make automating root cause challenging.

If you're running OpenTelemetry collectors against Azure Event Hubs, distributed partition ownership and checkpointing just got significantly better. Your fleet now self-organizes. Failover is automatic. Restarts don't lose data. Here's how we got here.

The software development lifecycle is collapsing. The multi-stage pipeline that defined how software got built and shipped for decades is compressing into rapid loops of intent and validation, with agents now part of the teams building and running it. Day 1 of Innovation Week was about what that shift means for how software gets validated, where observability fits, and the problems that have always been hard but are now genuinely urgent.

Modern operational environments are intricate ecosystems shaped by distributed architectures, accelerating change cycles, and a constant influx of telemetry. The complexity itself is not the issue. The issue is how teams construct understanding inside that complexity. After years of expansion across cloud, edge, third-party services, and internal modernization efforts, many organizations now have abundant data but limited confidence in the meanings behind it.

How quickly can you restore service when an incident hits your system? Most IT teams are not slowed down by detecting incidents. The challenge starts after something breaks, when the goal is to bring services back online as quickly as possible. Modern systems are highly distributed. Alerts arrive from multiple tools, dependencies are complex, and it is often difficult to immediately understand what actually failed.

When we shipped TV mode, we heard almost immediately: “Great, but I have five dashboards and one screen.” A single dashboard on a wall display covers one view of your infrastructure. If you want to rotate between your network overview, database health, application metrics, and infrastructure summary, someone has to walk over and click, or you’re buying more screens. Dashboard playlists solve this.

Claude on AWS now has two paths: Bedrock and the new Claude Platform. Compare pricing, billing mechanics, features, and cost visibility across both.

Claude API pricing per million tokens: Haiku 4.5 at $1/$5, Sonnet 4.6 at $3/$15, Opus 4.7 at $5/$25. Plus caching, batch discounts, and how to optimize API spend at scale.

Enterprises are pouring billions into GPUs and AI compute, but most are overlooking the infrastructure that connects it all. Justin Ryburn, field CTO at Kentik, makes the case that the network is the most underestimated variable in whether AI initiatives succeed or fail.