Applications today aren’t just a few servers with predictable traffic; they’re made up of hundreds of moving parts, spread across clouds, running on Kubernetes, and changing constantly.

Observability has always been about one thing: clarity. But in the age of microservices, Kubernetes, and distributed everything, clarity is harder and more valuable than ever.

Now, AI promises to bring speed, clarity, and even some predictions. The question is how much of that promise is real, and how much is just hype?

From anomaly detection to summarising noisy logs, AI is being pitched as the next big unlock in observability. And while the hype is loud, the reality is nuanced.

The Promise of AI in Observability

AI has shown tangible benefits across certain layers of the observability stack:

• Anomaly Detection: ML models can flag spikes in latency, drops in throughput, or weird request patterns that a human might miss.
• Log Summarisation: NLP-powered tools are beginning to cluster, group, and summarise logs, helping teams focus on broader trends, not just raw text.
• Noise Reduction in Alerts: AI systems can learn which alerts are actionable, which ones are false positives, and which combinations of signals really matter.
• Pattern Recognition Across Services: Especially in high-cardinality environments, AI can detect subtle trends across service interactions.

These are real wins, and they’re helping teams reduce Mean Time to Detect (MTTD), spot regressions faster, and find issues without brute force log digging.

So where’s the catch?

The Limits: AI Can’t Fix Bad Data

Here’s the thing: AI is only as good as the data it sees.

In most production systems, observability data is messy. Logs are unstructured. Traces are missing spans. Services don’t share consistent metadata. There's no context propagation. And the telemetry that AI depends on isn’t built with correlation in mind.

It means throwing AI on top of a broken or noisy telemetry foundation won’t magically produce insight. It will produce more noise.

That’s why AI looks great in demos but struggles in real systems, not because the AI is bad, but because it’s working with data that isn’t connected useful.

Clarity Still Needs Structure

AI excels at pattern recognition, but it struggles without structure, context, and intent.

For instance, your checkout flow is slow. AI might tell you there's a latency spike, and then what?

You'll still need:

• Logs correlated with traces
• Context propagation across services
• Clear service boundaries and ownership
• Runtime state visibility tied to each request

That’s architecture work, not algorithm work. AI can assist, but it cannot replace that foundational clarity.

CtrlB’s Approach: Focus on Fundamentals

At CtrlB, we believe in building for clarity first.

• We correlate logs and traces via context propagation
• We support schema-less, trace-aware querying at scale
• We enable natural language search on top of structured signals
• We keep high-value data hot, low-value data archived, no noise, just clarity

And yes, AI can sit on top of that. But only when the basics are clean.

The Road Ahead

AI in observability isn’t just hype. It’s evolving fast. LLMs are getting better at parsing logs. Correlation engines are learning smarter ways to prioritise alerts. And there’s real promise in copilots for incident response. But clarity still begins with your telemetry, not your tooling.

If you want AI to work for you, start by structuring your logs, tracing your services, and stitching your stack with context.

Observability isn’t about choosing between human intuition and AI assistance. It’s about enabling both by building a system that makes clarity inevitable.