From Observability to Actionability: Why Metrics Alone Aren’t Enough
Not too long ago, the cloud-native community declared victory on observability. We had our three pillars — metrics, logs, and traces — and a stack of CNCF projects and open source tools to collect them. Prometheus, Grafana, OpenTelemetry, Fluentd — observability became the buzzword of modern DevOps and platform engineering.
But as any SRE or platform engineer will tell you, the dashboards keep multiplying, the alerts keep firing, and the actual problems keep happening. We’re observing more than ever before, but are we actually doing more with what we see?
The uncomfortable truth: Observability has plateaued. We’ve mastered the art of data collection, but the real challenge now is turning that flood of telemetry into action.
A Short History of Observability
The first generation of monitoring was simple: uptime checks, CPU graphs, Nagios alerts. Useful, but blind to the complexity of microservices and distributed systems.
As Kubernetes and microservice architectures exploded, we needed something deeper. That’s where observability came in. Instead of just “Is it up?”, observability asked, “What’s really happening under the hood?”
- Prometheus gave us rich metrics.
- Fluentd and ELK let us index logs at scale.
- Jaeger and OpenTelemetry brought distributed tracing to life.
- Grafana gave us the dashboards to visualize it all.
The mantra of “logs, metrics, traces” became a cornerstone of the cloud-native ecosystem. And to be fair, it was a huge step forward.
The Problem: Metrics Without Meaning
But here’s the problem: more data doesn’t automatically mean more insight.
- Alert fatigue: Engineers are drowning in noisy alerts, most of which don’t require action.
- Dashboard sprawl: Every team builds their own panels, but few have a unified view of business impact.
- Correlation gaps: Logs, metrics and traces often live in silos — leaving humans to piece them together.
- No impact on MTTR: Despite more data, mean time to recovery hasn’t improved dramatically in many orgs.
In other words, observability has become a checkbox. Yes, you have the data. But can you act on it quickly, decisively and automatically? Too often, the answer is no.
The Shift Toward Actionability
The industry is starting to realize that observability alone isn’t enough. What we really need is actionable observability.
That means moving from data collection to decision support — and from decision support to automated action. Emerging trends point the way:
- AI-driven analysis (AIOps): Using machine learning to cut through noise, identify anomalies and highlight only what matters.
- Continuous verification: Linking observability to progressive delivery, so canaries and blue/green deployments are automatically promoted or rolled back based on live telemetry.
- Auto-remediation: When a known pattern is detected, the system takes corrective action — restarting services, rerouting traffic, or allocating resources — without waiting for a human.
- SLO-driven operations: Shifting from raw metrics (“CPU at 80%”) to service level objectives tied to user experience and business outcomes.
This is the difference between watching and acting.
Real-World Signals
We’re already seeing this shift in motion:
- OpenTelemetry is expanding beyond tracing to unify logs and metrics with semantic context, making correlation more actionable.
- Keptn and Argo Rollouts integrate observability into delivery pipelines, automating canary analysis.
- Cloud providers are bundling anomaly detection and AI-driven recommendations into their observability stacks.
- Forward-looking platform teams are using telemetry to not just alert humans but to trigger pipelines, policies and remediations.
The story is evolving from “what happened?” to “what should we do about it?”
The Platform Engineering Angle
This shift matters even more in the age of platform engineering. Internal Developer Platforms (IDPs) and golden paths are about removing friction for developers. And let’s be honest — developers don’t want to live in Grafana dashboards or comb through Kibana logs.
What developers want is simple: Feedback loops that are fast, relevant and tied to their code. If a deployment fails a canary test, roll it back automatically. If a service exceeds its error budget, stop shipping features until it’s resolved. If an anomaly is detected, surface a clear next step — not 50 charts to interpret.
Observability should become invisible infrastructure: Always there, always reliable, but surfacing only what matters in the workflow. Platform engineering has the chance to embed observability directly into golden paths, making it actionable by default.
Shimmy’s Take
For as long as I’ve been in security, we’ve been chasing the dream of “actionable intelligence.” The idea wasn’t just to collect logs, alerts and threat feeds, but to make sense of them, to prioritize them, and ultimately to act on them.
The reality, though, was different. For too long we’ve been drowning in logs, desensitizing our security and ops engineers to the very signals they needed to act on. We mistook collection for control.
With observability, we’re in a similar spot. We’re better than ever at collecting. The telemetry is there. The dashboards are beautiful. The traces are rich. But if we don’t combine that with meaningful, automated action, we’re just staring at pretty graphs while the system fails.
Actionable observability isn’t a “nice to have.” It’s the whole point. Without it, we’re wasting the very progress we’ve made.
Risks and Trade-Offs
Of course, we need to be thoughtful. Too much automation without trust can backfire:
- False positives could trigger unnecessary rollbacks or outages.
- AI-driven anomaly detection can be a black box — if engineers don’t trust it, they won’t use it.
- Human-in-the-loop matters: Not every decision should be automated.
The goal isn’t to eliminate humans, but to elevate them. Let the machines handle noise and repetitive fixes, while humans focus on judgment and strategy.
Closing Thoughts
Observability got us the data. That was the first big leap. But now the challenge is bigger: Making observability actionable.
The future of cloud native isn’t just about seeing more — it’s about doing more with what we see. That means tying telemetry to business outcomes, enabling continuous verification, automating safe responses, and yes, finally realizing the long-promised goal of actionable intelligence.
We’ve been stuck in the “data collection” phase for too long. It’s time to climb the next hill: Turning observability into action. Because in the end, nobody gets promoted for building beautiful dashboards. You get promoted for keeping systems reliable, users happy and businesses running. And that takes more than observability — it takes action.
