Operations | Monitoring | ITSM | DevOps | Cloud

The thing with Change Advisory Boards is that the intent was always good. Get smart people in a room, look at the evidence, and make sure nothing catastrophic goes out the door. In theory, that's hard to argue with. It doesn't scale in practice. Things happen between meetings. Teams rush to hit the window. The CAB meeting may not catch every risky deployment, but at least everyone can feel good about the process before the incident happens. Automated release management asks a different question entirely.

The traditional dashboard workflow, teams slowly handcrafting visualizations to track critical KPIs, is dying in a world of AI agents. A few years ago, in a pre-agentic-everything world, we tried to make it easier for developers to monitor critical experiences. We introduced Insights pages, which were pre-configured dashboards any Sentry user could adopt instantly that surfaced common health signals, like Web and Mobile Vitals.

Your customer started their renewal evaluation on a Tuesday in March. You did not know about it. Their CFO had asked the procurement lead to "look at alternatives" during a quarterly budget review. Three weeks later, a competitor's SDR was on a discovery call with their head of operations. By the time your CS platform's health score turned amber, six weeks had passed inside their building. This is what most CS leaders miss when they evaluate early warning systems.

Micro-frontend architectures, where independent teams build and deploy separate parts of a frontend application, introduce an observability challenge: Telemetry data is fragmented across services, making it difficult to determine which micro-frontend caused a performance degradation or error spike.

AI adoption is accelerating across organizations, and spending often follows a similar pattern: rapid growth, multiple providers, and limited visibility into where costs originate. Each provider exposes billing data differently, with distinct schemas, dimensions, and interfaces. FinOps and engineering teams often spend significant time consolidating fragmented data, only to end up with partial attribution and limited context about who or what generated the AI spending.

The uptime & availability of our status pages hasn't been great these past few days. The root cause is a persistent and pretty aggressive DDoS attack targeted at our own status page, status.ohdear.app. As a result, the overload on our systems also affected all other status pages we host for clients. We're not yet at Github or Claude levels of uptime sadness, but this isn't acceptable to us. In this post, I'll share what's happening and what steps we've already taken.

AI coding assistants have made it possible for a single developer to build and ship a production application in a weekend. Claude Code, Cursor, GitHub Copilot, and similar tools can scaffold a Rails app, write the models, generate the views, wire up the API, and push to production before Monday. This is genuinely exciting. It is also genuinely dangerous if you do not have monitoring in place before you ship.

Last updated: May 2026 If your team is 2 to 20 developers and you do not have dedicated DevOps, SRE, or platform engineering, most APM tools were not built for you. They were built for the team that has you: a team with specialists who can tune dashboards, configure alerting pipelines, manage data retention policies, and explain the monitoring system to everyone else. You do not have that team. You have developers who also handle deploys, on-call, and debugging production issues between writing features.

Have you ever considered how you can utilize artificial intelligence (AI) without sacrificing control over your data and autonomy? As we continue to navigate the changes of AI in the 21st century, it is important to understand how decentralized AI infrastructure can empower individuals and organizations to harness the potential of AI while maintaining sovereignty over their data and decision-making processes.