The healthy run is the one nobody instrumented
Every failure in our unattended pipeline has a paper trail. Stack traces, retry counts, timing deltas, the whole apparatus. We built that instrumentation the way everyone builds it: one incident at a time, each new probe installed the morning after something went wrong. Which means our telemetry is a museum of past failures. It describes, in exhaustive detail, the specific ways this system has…
In a system where failures are meticulously recorded, a lack of instrumentation for successful runs presents a glaring oversight. The telemetry data primarily captures the moments of failure, offering an exhaustive account of the ways in which the system has broken down. This telemetry, while comprehensive in its detailing of past breakdowns, fails to provide insight into the system's usual functioning state.
In the majority of its operation, the system is quiet and uneventful, and as such, its health remains largely invisible and opaque.
A reporter encountered this issue while investigating whether the agent was exerting more effort than before. The agent's performance had noticeably deteriorated, with longer reasoning chains, increased tool calls per task, and more tokens being consumed to achieve the same output. The system's success was defined by the absence of anything worth logging, leading to an incomplete understanding of the system's health when it operates smoothly.
This definition of health as the absence of problems rather than the presence of optimal functioning implies that the system is only legible when it is ill.
Over time, this lack of instrumentation has led to a phenomenon the reporter refers to as "drift-shaped" problems. These are issues that go unnoticed until they reach a critical threshold, as there are no alerts or signs of trouble until the change becomes significant enough to disrupt the system's operation. The reporter observed this with prompt outputs, where the agent gradually produced longer outputs over a period of a month, a subtle inflation that went undetected.
The absence of a length budget meant that no one had documented what constituted "normal" output length, making it impossible to identify the change's onset.
The reporter suggests a more targeted approach to monitoring. Instead of logging everything, which leads to an overwhelming and unproductive data firehose, one should select a few key metrics to define the shape of a successful run. These could include duration, output size, number of tool invocations, and retry count—even when it is zero.
By focusing on these aspects and analyzing the distribution of these metrics rather than individual values, one can detect subtle shifts in the system's performance. A single run offers limited insight, but a comparison across many runs can reveal whether the median output is moving in an undesirable direction. This approach acknowledges the importance of an unattended system's baseline knowledge, which a human operator inherently possesses but that disappears when the system runs without supervision.
The critical question for any autonomous setup is not about the alerts that might be triggered, but rather how long it would take to identify if the system were to degrade by twenty percent without any failure being reported. The answer to this question highlights the gap in our current monitoring capabilities and the critical need for a more nuanced and comprehensive system of checks and balances.
Written by urgent.news from Dev.to's reporting — not their text. Machine-written — may contain errors; check the original before relying on it.
