Operations | Monitoring | ITSM | DevOps | Cloud

In the first experiment, I wanted a baseline: if an AI coding agent gets the same production signal a human would get, can it fix bugs in a codebase it has never seen? Yes, but only when I gave it better context. With only an alert, the agent passed 51% of the runtime tests. When I added captured traffic, the actual request and response for the failing call, it climbed to 77%. This post is the second pass.

Network admins want all devices with the same role to behave the same way. But in real environments, that consistency rarely lasts. Imagine two core switches in the same data center. They serve the same function and run the same OS version. One handles traffic without issue, while the other drops packets during peak hours. Logs show nothing obvious. Routing looks correct. The team spends hours checking links, hardware, and traffic paths.

For many EUC and digital workplace leaders, the challenge isn't a lack of technology. It's understanding why workplace issues continue to surface despite years of investment in automation, AI, and digital transformation. Support teams are still dealing with high ticket volumes. Rollouts intended to improve employee experience can create new sources of disruption, and IT often struggles to understand what employees are experiencing until problems escalate into complaints, incidents, or support requests.

Organizations are no longer asking whether to adopt AI; that question is settled. The focus now is on reaching a point where AI is doing meaningful operational work—or as the industry calls it, being “AI-first.” But being “AI-first” isn’t binary. You don’t go from zero AI to meaningful autonomy by flipping a switch. In reality, getting there means moving through distinct stages.

Traces are a goldmine of information that can help you, or your AI, find slow pages and fix them. Next.js comes out of the box with support for tracing. Incoming requests, fetch() calls, middleware, and server-side rendering are all wired up and ready to send traces to any OpenTelemetry-compatible backend. The catch is, unless you configure an exporter, you’ll never see those traces.

The World Cup is already proving how quickly digital demand can concentrate across Mexico’s networks, making internet path visibility critical for teams responsible for reliable user experiences. The 2026 FIFA World Cup is already testing Mexico’s networks. Mexico’s June 11 opening match against South Africa drew 7.1 million viewers for an English-language U.S. broadcast and peaked at 9.1 million viewers. That kind of demand puts real pressure on the systems behind digital experiences.

TL;DR Agentic observability uses AI agents to autonomously investigate incidents, identify root causes, and take action in production environments. Unlike traditional monitoring (which alerts and waits) or AIOps (which assists human analysis), agentic platforms conduct the investigation themselves. Key capabilities include autonomous incident triage, evidence-backed root cause analysis, alert noise reduction, and governed remediation.