AI
How Causal Reasoning Addresses the Limitations of LLMs in Observability
Causal reasoning with AI agents enable proactive incident prevention, automated remediation, and a path toward autonomous service reliability.
observability
Blog
Demystifying Automatic Instrumentation: How the Magic Actually Works
Most developers use automatic instrumentation without knowing how it actually works. This post breaks down the key techniques behind it—not to build your own, but to understand what’s really happening when things "just work."
Causality
The Signal in the Storm: Why Chasing More Data Misses the Point
More telemetry doesn’t guarantee more understanding. In many cases, it gives you the illusion of control while silently eroding your ability to reason about the system.
observability
When Everything Is Instrumented, and You Still Don’t Know What’s Broken
In 'Rethinking Reliability for Distributed Systems,' Endre Sara shared a common story: a large-scale customer, running mature microservices in Kubernetes with full observability coverage, still struggles to understand what’s broken during a high-stakes business event.
observability
Making Observability Work: From Hype to Causal Insight
A few weeks back, I joined Charity Majors, Paige Cruz, Avi Freedman, Shahar Azulay, and Adam LaGreca for a roundtable on the state of modern observability. It was an honest conversation about where we are, what’s broken, and where things are heading. You can read the full summary on
Causality
If Planes Can Fly Themselves Then Why Can’t IT Management be Autonomous?
When it comes to observability and IT operations, our goal should be to get humans out of the loop as much as possible.
Webinar
From Visibility to Control - Key Takeaways from a Fireside Chat with Viktor Farcic & Shmuel Kliger
“You actually cannot do meaningful reasoning especially when it comes to root cause analysis with LLMs or machine learning alone. You need more than that.” -Shmuel Kliger, Founder of Causely