Operations | Monitoring | ITSM | DevOps | Cloud

The request timed out after five seconds. The application said context deadline exceeded. The dependency was healthy, its dashboards were green, and its own latency histogram showed nothing above 30 milliseconds.

Synthetic monitoring has been a critical part of application reliability for years. It gives engineering and operations teams a way to proactively test applications, APIs, and critical customer journeys before users encounter problems. But there is a fundamental limitation with the traditional approach: Someone has to create the tests. As applications become more distributed and customer journeys become more complex, organizations can end up maintaining hundreds or even thousands of synthetic scripts.

Long before dashboards and decibel-loud alerts, there were watchtowers. Every kingdom worth its salt had them, men perched on hills, lighting fires to signal the moment they spotted something suspicious on the horizon. It was, in its time, a fine system. The trouble was that watchmen, being human, occasionally mistook a herd of cattle for an invading army, or a dust storm for smoke, and lit their fires anyway.

Modern IT teams invest heavily in monitoring infrastructure, applications, servers, and network devices. Yet when users report that a critical cloud service is slow or a branch office loses connectivity, one question often remains difficult to answer: where is the problem actually occurring? Is the issue inside your network? Is it your ISP? Has a routing change introduced excessive latency? Did an upstream provider experience an outage?

Discover how Megaport and Wasabi are expanding cloud object storage to support AI workloads, enterprise data growth, and predictable costs.

Agentic IT operations have arrived. It’s no longer a question of if enterprise IT departments will adopt agentic ITOps, but how quickly. The question we hear most often at BigPanda isn’t “what are agentic ITOps,” it’s “what data do we actually need to get started?” That’s the right question to ask. Agentic AI is only as good as the data and context that feeds it. Real-time observability and telemetry data from machines. Structured ITSM and workflow records.