Operations | Monitoring | ITSM | DevOps | Cloud

The latest News and Information on Monitoring for Websites, Applications, APIs, Infrastructure, and other technologies.

Introducing Obkio's Remote User Monitoring Plan: For Distributed Workforces

The way we work has fundamentally changed. Remote and hybrid work aren't temporary shifts; they're the new reality for most organizations. And with that reality comes a challenge IT teams know all too well: how do you troubleshoot network issues for users you can't physically reach?

From Atlassian JSON to Actionable Audit Insights

Atlassian audit logs carry high-value security and operational signals, yet the raw format makes them hard to use in day-to-day investigations. Nested JSON, arrays inside arrays, and localization keys turn routine questions into slow, manual work. For lean Security and IT teams, that friction shows up as delayed triage, fragile dashboards, and alerts that fire without enough context to act.

From Ukraine to the Cloud: Stories of IPv4 Migration

This post expands on our analysis from last year that revealed that as much as 20% of IPv4 space has migrated out of Ukraine in the years following the Russian invasion in February 2022. This update reveals that AT&T (a popular destination for Ukrainian IPs) has since implemented a policy ridding itself of customers using AS7018 to originate their routes, often to support residential proxies.

Scaling AI Reliability: Real world lessons from Mistral AI

How does one of the world's leading AI companies keep its infrastructure reliable while shipping new models constantly? In this webinar, Devon Mizelle, Senior SRE at Mistral AI, shares the real story. Devon walks through how Mistral built an automated system that generates synthetic checks for every model the moment it goes live—no manual configuration, no forgotten monitors, no inconsistent alerting. Using monitoring as code, his team eliminated the toil of maintaining hundreds of checks across a rapidly evolving model ecosystem.

Log Drains Now Available: Bringing Your Platform Logs Directly Into Sentry

Sentry now supports log drains, making it easy to forward logs into Sentry without any application code changes or manual project-key lookups needed. If your logs already exist somewhere else, you can now see them alongside errors and traces in Sentry, no code changes required. Already want to get started? The quickstart guide is one click away.

Why Context, Not Prompts, Determines AI Agent Performance

Prompt engineering improves single responses, but agent performance is determined by how execution context is captured, replayed, and constrained over time. For the past few years, enterprises have obsessed over prompts, with entire roles emerging around their design and an ecosystem of tooling and templates following close behind. This focus delivered early gains because it allowed teams to rapidly improve outputs without modifying the surrounding system. Over time, those gains flattened.

Datadog acquires Propolis

Generative AI enables teams to write and ship code faster than ever. But current methods for testing and quality assurance have not evolved to match the new pace and scale of deployments. Manual and deterministic testing paths quickly become obsolete when new features are released, and they fundamentally can’t test AI outputs, leaving a massive untested surface area. To keep up, teams need new testing methods that can define what goals users have, and ensure that their outcomes match.

AI is not intelligent. It's obedient.

Tech companies and brands love calling AI “intelligent.” But is it really? AI doesn’t decide what matters. Humans do. We decide what’s important, then feed prompts, data, and instructions into AI models so they work the way they do. At the end of the day, AI is obedient to human intelligence, not the other way around. And it’s on us to use it in ways that actually matter, instead of dismissing it or freaking out that it’s going to replace humans.

What API Performance Monitoring Looks Like in Real Production Environments

API performance monitoring has become a critical discipline for modern engineering teams, but most conversations around it stop at metrics, dashboards, and testing tools. Teams measure response time, track error rates, and run performance tests before release, yet APIs still slow down, silently fail, or violate SLAs in production. The problem isn’t a lack of monitoring. It’s a mismatch between how APIs are tested and how they actually behave in the real world.