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Metrics & Mayhem

by Al Mann

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Latest episodes

Wide Events: Observability 2.0, Without the Hype

August 11, 2026 · 20 min

Everyone is talking about observability 2.0 and wide events. This is the plain-English version, and the honest part: they are powerful, and they are not new. I cover what a wide event actually is, why it answers the questions logs and…
You Don't See the Foundation: Why AI Ops Fails Before It Starts

August 7, 2026 · 14 min

The demo shows you the tower, the agent that fixes the incident on its own. It never shows you the year underground that made it possible: the clean telemetry, the tags, the traceability. This Signal Drop is about why AI ops is only ever…
OpenTelemetry Profiles: The Fourth Signal, Explained

July 28, 2026 · 14 min

Metrics, logs and traces tell you the box is hot. They cannot tell you which function is burning the CPU. OpenTelemetry Profiles, the fourth signal, finally can. In this Tech Tuesday, Allan Mann explains continuous profiling: what an…
The Gate Stays Human: Who's Accountable When AI-Ops Acts

July 24, 2026 · 12 min

When can you let an AI agent act on its own in production, and when does a human have to stay on the gate? This Signal Drop gives you the whole AI-ops governance model in one line: you decide what to automate by blast radius, not by how…
Quiet Isn't Good: How to Justify Observability Spend

July 17, 2026 · 11 min

Your observability is working, nothing is on fire, and that is exactly when someone asks why you are still paying for all of it. This Signal Drop is about the prevention paradox: the better your monitoring works, the harder it becomes to…

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How Do You Observe an AI Agent? | Tech Tuesday 02

July 14, 2026 · 9 min

AI agent observability is the missing camera on the agents now acting in your production systems. AI agents are moving beyond answering questions. They investigate incidents, call tools, analyse production data and, in some cases, apply…
Signal Drop 23: The Curiosity You Stop Needing

July 10, 2026 · 8 min

Good tools answer the question so fast you forget you were the one who asked it. Dashboards, runbooks and AI incident summaries are all useful, and all quietly retire the questions you used to ask yourself. Curiosity is a muscle: when your…
What Is MCP? The Model Context Protocol, Explained | Tech Tuesday 01

July 7, 2026 · 10 min

MCP, the Model Context Protocol, is the open standard for connecting AI models to your tools and data. In the first Tech Tuesday, we explain what MCP is, the problem it solves, and where it fits in 2026, in plain terms. Introduced by…
Signal Drop: The Six-Week Decay

July 4, 2026 · 7 min

Automate incident triage, and your best responders quietly lose the reps that kept them sharp, until the automation paradox meets a novel fault. I once watched one of the best incident teams I have worked with get quietly worse over about…
Signal Drop: You're Going To Have To Suffer Today

June 26, 2026 · 11 min

The launch pre-mortem: you can't promise a quiet launch, but you can give your team the bad night in advance. Tested, green dashboards, everyone is confident. It wobbled forty minutes in, and the team fell apart, not because the wobble was…
Signal Drop: Anger Is Just Fear in a Hi-Vis Vest

June 18, 2026 · 6 min

Sev1, and someone's typing in capitals. This should never have shipped. Who approved this? The channel tightens, the debugging slows, and now everyone's managing the angry person as well as the outage. I heard someone this week say anger…
Signal Drop: The Cave You Won't Instrument

June 12, 2026 · 7 min

I avoided building one dashboard for about a year. The cost-per-request board for a service I already suspected was showing a bad number. Every deferral had a reason. Every reason was a way of not looking. Joseph Campbell: The cave you…

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About the show

Metrics & Mayhem is a straight-talking podcast on leadership under pressure for leaders and teams in banking, finance, and software. Weekly Signal Drops deliver quick insight, with longer episodes when a topic needs room. Built from years of operations (keeping services running) and observability (understanding what systems are doing), the rule is simple: clarity beats noise. No jargon. No fluff. Just signals that matter, even when the dashboards look smug.