Observability is at a crossroads. We’ve spent years building tools to observe our systems, understand their behavior, and catch their hiccups. But something bigger is happening—observability itself is changing. It’s no longer just about monitoring systems; it’s about understanding change itself. As our systems evolve and our needs shift, so too must our approach to seeing and understanding them. What are we looking for when we observe our systems? And how is our very notion of observation transforming with the systems we build?

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.

But this raises a fascinating philosophical question: What exactly are we observing when we track change? Traditionally, we’ve had two ways of seeing the world: as a collection of things (objects, structures, states) or as a flow of processes (dynamics, transformations, behaviors). It’s like the age-old debate between seeing a river as a thing you can step into, or as Heraclitus noted, as a constant flow where you never step into the same river twice.

The beauty of focusing on change is that it bridges these two views. When we observe our systems, we’re simultaneously tracking both the things that change (the services, the databases, the metrics) and the processes of change themselves (the flows, the interactions, the transformations). Change is the lens that lets us see both the dancer and the dance, both the river and its flow. This dual nature of change helps explain why some teams focus on state-based monitoring while others emphasize event streams and traces—they’re looking at different aspects of the same underlying reality

But not all change is equal. Think about it like layers in a cake—at the bottom, we have the simple differences between two measurements, what we might call “happened-change.” A trace, for instance, is a flicker of activity—a transaction’s journey through a system. It’s measurable, but is it meaningful? These basic changes are like individual notes in a symphony. The magic happens when we start looking at how these changes relate to each other and how they signal larger transformations. Just like your smartwatch doesn’t just measure heartbeats but tries to understand your overall health and activity, our systems need to connect individual changes to broader patterns and purposes. This raises a critical question: What’s the true subject of observability? Are we observing the traces themselves, or are they merely clues to something more enduring—the system as a whole?

Systems are more than the sum of their parts. They’re dynamic, evolving entities shaped by the changes that flow through them. Like a river carved by its currents, systems are defined by interactions, feedback loops, and relationships. Each change builds upon past changes, creating either virtuous cycles of growth or vicious cycles of decline. It’s like compound interest for system behavior—small improvements or degradations accumulate over time, gathering momentum in their chosen direction.

As these systems grow, they develop new behaviors that couldn’t be predicted by looking at individual components alone. It’s like watching a murmuration of starlings—thousands of individual birds creating patterns that emerge only at scale. But here’s the fascinating part: as organizations mature, they discover that pure observability isn’t enough. They need to move toward monitoring and controllability, finding that delicate balance between enabling growth and maintaining stability. It’s like learning to ride a bicycle—at first, you observe and react, but eventually, you develop an intuitive sense of control that lets you navigate more smoothly.

This brings us to the core challenge: How do we make sense of change in complex systems? Change isn’t just about difference—it’s about the difference that makes a difference. It’s about finding those pivotal moments, those key shifts that ripple through the system and matter to humans. After all, what good is measuring a thousand tiny changes if none of them help us understand or improve what matters? Every change comes with a cost, but the hope is that it brings value and increases our agility—our ability to change even better next time.

Think about it like reading a story: not every word carries the same weight. Some sentences change everything—they reveal the plot twist, define a character, or shift our entire understanding. In our systems, we need to develop this same narrative intelligence, this ability to spot the changes that truly move the plot forward. With countless traces, logs, and metrics, how do we separate these meaningful signals from noise? How do we identify the changes that lead to growth rather than decline?

The answer lies in abstraction and intention, but more importantly, in understanding what constitutes meaningful change for humans. We can’t observe every detail; we’d drown in data. Instead, we must focus on the changes that transform understanding or enable action—the differences that make a difference in how we perceive, maintain, and improve our systems. As our systems grow more complex, this becomes both more challenging and crucial. The signals that matter might be spread across more components, or visible only at certain scales of operation.

It’s like developing a new sense—at first, everything seems equally important, but gradually you learn to filter, to focus, to find the patterns that matter. This isn’t just about having better tools; it’s about evolving our very understanding of what we need to see and why. Like a fitness tracker that doesn’t just count steps but helps you reach health goals, good observability connects measurements to meaningful outcomes. It helps us see not just what changed, but what that change means for the people who depend on our systems, and most importantly, what changes we might want to make next.

And it’s not just about individual understanding—it’s about how teams and organizations collectively make sense of their systems. Observability becomes a shared language, a way for different minds to align their mental models. When an incident strikes at 3 AM, it’s not just about the alerts and dashboards—it’s about the shared stories and patterns that help teams navigate the chaos together. Like ancient sailors reading the stars, we develop collective wisdom about our systems‘ behavior.

But here’s where it gets interesting: as organizations mature, they discover that observation and intention dance together in unexpected ways. Better observability leads to better understanding, which enables more intentional changes, which in turn creates systems that are easier to observe. It’s not just a virtuous cycle—it’s a transformation in how we think about change itself. We move from passive observers to active participants, from observing changes happen to orchestrate the changes we want to see.

Think about how your understanding of a city changes as you spend time in it. At first, you notice the obvious things—the main streets, the landmarks, the busy intersections. But as you live there longer, you start to see the subtle rhythms—the morning rush patterns, the weekend quiet, the seasonal shifts in energy. Our systems are like that too. As they grow more complex, what we need to observe evolves. The metrics that served us well when our system was simple might become noise when it scales. The patterns we thought were significant might turn out to be mere surface ripples above deeper currents.

That’s why mature organizations learn to balance the drive for innovation with the need for stability, moving from pure observability to a more nuanced approach that includes sensing and steering. They understand that observation isn’t an end in itself—it’s a tool for intentional evolution. Every alert, every dashboard, and every metric should connect to something we might want to change, improve, to transform.

As engineers, we’re wired to dig into details, hunt for root causes, and fix problems at their source. This mindset makes us effective troubleshooters, but it can also trap us in the weeds. We celebrate the hero who dives into logs to save the day, but we rarely reward those who prevent fires from starting in the first place. Our industry often prioritizes reactive problem-solving over proactive system stewardship. Observability, however, invites us to shift this mindset.

So, how do we reframe observability? It starts by recognizing that it’s not just about finding problems—it’s about understanding systems. It’s about seeing how they evolve, how they adapt, and how they interact with their environment. It’s about asking, “What is this system trying to tell me? What’s truly changing, and why does it matter?” This requires a cultural shift: from firefighting to system thinking, from control to trust.

The systems we build are too complex for any one person to comprehend fully. And that’s okay. What’s crucial is how we might transcend these limitations. Just as humans use dreams and games to simulate experiences beyond our physical constraints, we can create simulated spaces where our systems can evolve beyond their structural boundaries. In these spaces, we can observe and experiment with new behaviors, free from the constraints of production environments. The goal of observability isn’t just to see every detail—it’s to see enough to guide the system’s evolution, to help it transcend its current form while maintaining its essential purpose. This demands trust: in our tools, in our abstractions, and in ourselves to know when to step back and see the forest, not just the trees.

Ultimately, observability is more than a tool or a practice—it’s a mindset. It’s a way of thinking about systems, change, and the relationships between parts and wholes. It’s about finding meaning in the chaos, seeing patterns in the flux, and using that understanding to shape the systems we create. And perhaps, if we’re lucky, it’s also about seeing ourselves more clearly—not just as builders of systems, but as participants in a larger, ever-changing world. After all, aren’t we all both observers and observed, changing and being changed, in this grand dance of existence?