Observability First: How I Instrument New Features Before They Ship

Observability First: How I Instrument New Features Before They Ship

Shipping something new is fun. Debugging it blind in production? Not so much.

Over the years, I’ve learned that the best time to add observability is before your code ever sees real traffic. Instrumentation shouldn’t be a cleanup step or a postmortem action item — it should be part of the feature itself.

Here’s how I build with observability first.

1. Define “What Good Looks Like” — Then Make It Measurable

Before I write a line of code, I ask: What should this feature do when it’s working well? And how would I know if it’s not?

That turns into things like:

• Expected usage patterns (e.g. request volume, success/failure rates)
• Performance expectations (e.g. latency, cache hits, DB query times)
• Edge conditions (e.g. empty inputs, rare configs, error fallbacks)

These become the foundation for the metrics, logs, and alerts I design alongside the feature.

You can’t monitor what you don’t define. So start by defining “healthy.”

2. Instrument the Happy Path and the Edges

A common mistake: adding a single success metric and calling it a day.

Instead, I aim to capture:

• Successes (counted and tagged)
• Failures (with structured reasons)
• Skipped paths (why this code didn’t run)
• Timing data (to detect latency regressions)

When possible, I also add counters for fallbacks, retries, or degradation modes — anything that tells the story of how the system behaves when things get weird.

3. Emit Context That Makes Debugging Possible

Good logs and metrics don’t just say what happened — they say what was happening around it.

So I include:

• Request IDs, trace IDs, or correlation tokens
• Config inputs (e.g. feature flags, environment settings)
• Key entities (e.g. customer ID, campaign ID, page type)
• A known set of log messages with consistent structure and phrasing

This helps me search logs easily and cross-reference incidents across services.

Think of your future self at 2AM. What context will they need to make sense of this?

4. Bake Observability into the Rollout Plan

Every feature rollout plan should include:

• Dashboards showing usage, success/failure, latency
• Alerts for critical thresholds (with paging or ticketing levels)
• Smoke test monitors to validate post-deploy health
• Query bookmarks for common triage paths

I usually build or update these before launch. That way I’m not trying to do it in the middle of an incident.

5. Socialize It with the Team

If I’m adding a new metric or log structure, I make sure it’s documented or demoed:

• Slack post with dashboard links
• README updates in the repo
• Screenshots in the feature rollout doc
• Shared queries in our observability tool

This makes it easier for my teammates — and my future self — to find and use the data.

Final Thoughts

Instrumentation isn’t overhead — it’s how you buy visibility, trust, and sleep. When observability is built in from the start, you don’t just ship faster — you ship with confidence.

So every time I build something new, I ask myself:

“If this breaks in production, how long will it take us to know? And how easily can we tell why?”

That’s when I know the feature is really ready to ship.