In an industry that prides itself on innovation and progress, the observability sector stands as an anomaly - a field seemingly trapped in amber, where genuine evolution has been replaced by an endless cycle of marketing-driven rebranding. Despite decades of supposed advancement, the fundamental approaches to understanding system behavior remain stubbornly unchanged, masked only by increasingly elaborate terminology and ever-growing data collection.
Author: William David Louth
From Mechanical Sympathy to System Sympathy
Software and systems performance engineering needs to undergo a fundamental shift. While the traditional focus on hardware optimization—known as mechanical sympathy—remains valuable, the increasing complexity of modern distributed systems demands a more comprehensive approach. This document outlines system sympathy, a new mindset for understanding and optimizing system-wide performance in enterprise environments.
Preserving Situational Awareness in AI-Assisted Software Development
The future of software development doesn’t lie in relinquishing our understanding to AI, but rather in establishing a genuine partnership that augments our cognitive faculties while preserving the profound comprehension that enables the creation of exceptional software. As we progress with the integration of artificial intelligence, maintaining this equilibrium between automation and awareness will be paramount for the ongoing evolution of our discipline.
Simplicity is Intelligence
Simplicity isn’t the antithesis of complexity; rather, it’s the outcome of acquiring knowledge from complexity. True learning doesn’t entail accumulating an excessive amount of information or processes. Instead, it entails comprehending patterns sufficiently deeply to simplify them, thereby integrating them into our fundamental understanding.
Observability X – Substrates 101
We’re almost done with this series, and we’ve covered some rather profound concepts. Let’s take a quick look at what we’ve learned so far. We’ll break it down into two parts: form and function.
Observability X – Composers
The Substrates API enables the direct utilization of Pipes and Channels, while simultaneously offering a mechanism to construct percepts that adorn these fundamental elements of any sensing and synthesis pipeline. To construct a Conduit that facilitates the on-demand creation of percepts upon receiving a name, we must provide a Composer to the conduit method within the Circuit interface.
Observability is about (to) Change
At its heart, observability is about change. Without change, there's nothing to observe. A static system, frozen in time, offers no insights. Time itself is meaningless without change—it’s merely a measure of movement, of transformation. A clock ticks because its hands move; atoms vibrate. Change is the pulse of existence. So, when we talk about observability, we’re exploring how we perceive and interpret change within the systems we design.
A General Theory of System Observability
We need a unified theory of observability to address the limitations of current practices while still being practical and straightforward to implement. It should go beyond different areas of the system, like service monitoring, user experience, and system changes. It should combine quantitative measurements with qualitative understanding while connecting with context.
AIOps – SignOps + TaskOps
Organizations adopting AI for cloud operations face a critical evaluation of AIOps approaches. While recent advancements in AIOps frameworks standardize AI agent evaluation and enhancement, they risk a dead-end relying on conventional observability models lacking semantic depth. Service Cognition principles should form the AIOps foundation for truly autonomous cloud operations.
Service Cognition – Quantitative to Qualitative
Organizations are drowning in an ocean of metrics. Every click, interaction, and transaction generates quantitative data points, creating vast repositories of numbers that promise insights but often deliver confusion. The challenge isn’t just the volume of data – it’s the fundamental question of how to transform these raw metrics into meaningful qualitative understanding.