in Blog
I know. It’s April 1st. You’re already skeptical. That’s fair. The AI hype cycle has produced more headlines than outcomes, and announcing an “AI marketing teammate” on the internet’s most gimmick-prone day feels like exactly the kind of thing you’d scroll past. So let me get straight to the point:…
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in Blog
You know the number. Your team spends 80% of their time fighting fires instead of preventing them. Alert triage, log parsing, correlation, manual investigation. Hours that could go toward building better systems instead get buried in reactive drudgework. The cost of downtime isn’t just outages, it’s the engineering time spent…
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in Glossary
As AI systems become more capable, they also become more dependent on external tools, data sources, and workflows. A model alone cannot investigate incidents, query infrastructure, access databases, or interact with enterprise systems or other agents without a structured way to connect to them. That’s where Model Context Protocol (MCP)…
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in Glossary
SRE toil refers to the repetitive, manual, and low-value work that consumes a disproportionate amount of time for site reliability engineers. In the context of modern systems, SRE toil often includes tasks like investigating alerts, correlating signals across tools, executing runbooks, and manually diagnosing incidents—activities that are necessary but do…
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in Glossary
MTTR (Mean Time to Resolution) is one of the most important metrics in site reliability engineering (SRE), measuring how quickly teams can restore service after an incident. Traditionally defined as the average time it takes to repair or recover from a failure, MTTR is widely used to evaluate incident response…
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in Glossary
Root cause analysis (RCA) is the process of identifying the underlying cause of an incident, not just the symptoms, so that it can be permanently resolved and prevented from recurring. In modern systems, this goes beyond simply fixing what’s broken; it requires understanding how complex dependencies, changes, and behaviors interact…
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in Blog
We’ve spent the last decade solving the visibility problem. Most modern systems are deeply instrumented. Request rates, error counts, tail latency, deploy markers, dependency health, infrastructure saturation — it’s all there. In many environments, the question isn’t whether you have signals. It’s whether you have too many of them. This…
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