Operations | Monitoring | ITSM | DevOps | Cloud

Builder in the Loop is a Mezmo interview series focused on the engineers, product leaders, and operators shaping AURA, an open-source, MCP-native agent harness for production operations. The goal is to get past the polished product layer and talk through the decisions that matter when AI starts interacting with real systems. Key questions include: What should agents be allowed to do? How do they get better over time? Where should humans stay in the loop?

It’s 20 minutes into a P0 incident, and you have already switched between four different tools, re-authenticated twice, and translated queries across three incompatible syntax languages. The root cause you are searching for. Well, that is still out there somewhere. The reality of investigative latency is that most engineering teams face navigation problems, not data problems. During high-pressure incidents, teams lose cognitive momentum due to context switching between disconnected telemetry silos.

There is a moment every engineer knows too well. Something is wrong in production. You have an alert, a vague symptom, and pressure to find the one signal that explains what changed. You open your logs and traces, and you immediately hit the same two problems: the dataset is huge, and the path from “I see something odd” to “I understand why” is full of tiny, exhausting steps. Meet new Explore, our redesigned investigation experience for logs, traces, and spans.

OpenTelemetry is one of the best things that’s happened to observability in the last decade. It’s open. It has SDKs for every language that matters. It’s vendor neutral. The OTel community has been doing the hard work of standardizing how applications emit telemetry, so that you, the engineer, don’t have to learn five different agent formats to monitor five different services.

Every single day, the Sumo Logic Platform analyzes more than four exabytes of log data. The good news? The answers to your application performance, infrastructure health, and security incidents are hidden in those logs. The challenge? Historically, uncovering those answers required query language fluency. That’s why we built Mobot, our conversational interface that connects users to advanced AI capabilities using natural language.