Observability

The debugging loop is slow. You cannot reproduce the bug locally, so you add a log line and redeploy. Wrong place. You do it again. An agent alone does not fix this: it still has to pick a mechanism, and it has to know what that mechanism costs. This talk is about three approaches that take the source edit out of that loop. Each acts at a different point in the software lifecycle, and each inherits the strengths and constraints of its intervention point. ...

A benchmark is a measurement system. It can report a precise number while measuring removed work, an unstable sample, or an uncontrolled machine. This talk argues you should trust a Go benchmark only after answering three questions: Is the compiler measuring real work? Is the sample stable enough? Is the difference large relative to the noise? We work through three layers. Compiler honesty covers dead-code elimination, sinks, constant folding, inlining, timer ordering, and the B.Loop construct, which manages the timer for you and keeps the loop body from being optimized away. Statistical interpretation covers repeated samples, benchstat, coefficient of variation, run-count discipline, and the p-hacking traps that inflate false positives. Environment control covers both local machines and CI: Linux frequency and isolation controls, perflock, benchdiff, and the bare-metal runners that shared CI instances are not. ...

Jonathan Hall and Shay Nehmad had me on Cup o’ Go for episode 167, straight after their GopherCon US recap. We spent about forty minutes on instrumentation: what an APM tool is actually measuring, and the three ways you can get telemetry out of a Go program without asking every team to rewrite their handlers. The part I enjoyed arguing about most was blast radius. Manual instrumentation is precise and tedious, and it never finishes. eBPF buys you one agent covering every language on the box, at the cost of running code in the kernel, where a bad program can take the whole host down with it. Compile-time instrumentation rewrites the syntax tree behind go build through the -toolexec hook, which keeps failures scoped to one service and works anywhere Go compiles. People love this dark magic, which I find either reassuring or alarming depending on the day. ...

You can’t reproduce the bug locally, so you add a log line and redeploy. Wrong place. You add another one and redeploy again. That is the debugging loop from my GopherCon UK talk, and in this last part we’re going to shorten it: a decision rule for two zero-touch tools, then a runbook an AI coding agent (not a Java-style one) can execute. The runbook starts with one boring question: is this Go service already running, or can I rebuild it? If the service has to stay untouched, OBI (OpenTelemetry eBPF Instrumentation) is the default. If I can rebuild it and need function-level spans, otelc (OpenTelemetry Go compile-time instrumentation) is the default. The rest of the decision tree is checking privileges, Go version, collector endpoint, and whether RED metrics are enough. ...

Every route in this series goes around the Go runtime: rewrite the source at build time, inject something at process start, or watch from the kernel, because Go gives us no agent API to plug into. This time we ask the runtime what it could hand us. The honest summary for 2026: one long-awaited feature shipped, two open proposals document gaps that have caused real production problems for years, and two experiments of mine are aspirations, not proposals. ...