Code That Reveals Itself: Designing for Observability at the Function Level

Stop writing code that hides intent. Start building systems that narrate their own behavior - and survive scaling.

We spend so much time debating clean code. But the code that causes the most pain - the one that quietly drains hours - is the code that hides its intent.

This isn't about bugs. It's about code that works but can't be understood without going through four files and a debugger.

As a senior full stack developer and devtool builder, I've learned that the best code isn't just correct. It's self-explaining. It reveals what it does and why - especially when things go wrong.

This article walks through that journey: why we don't design for observability at the function level, how the pain shows up, and how I build systems today that explain themselves.

Why We Don't Do It By Default

When code works, we move on. We build useCustomerInfo(), it returns { data, loading, error }, and life goes on.

But a few weeks later:

• It sometimes fetches again unexpectedly.
• Data is stale and we don't know why.
• The retry logic behaves weirdly but only in some environments.

So we open 4 files, put breakpoints, and try to guess what's going on. We read function names and hope they match behavior.

"The function wasn't broken. It just wasn't saying anything."

When the Need Appears

This pattern becomes obvious once you hit scale:

• Hooks are reused across contexts.
• A colleague uses your code but doesn't know the assumptions you encoded.
• Side effects stack up: fetches, cache reads, timeouts, cleanups.

Suddenly, understanding what a function does is easy. But understanding why it behaves a certain way at runtime? That's the real challenge.

This is when you stop needing clean code - and start needing observable code.

A Complete Example: From Opaque to Observable

Let's take a real-world scenario. You have a hook that fetches a customer's data. Here's how it typically starts:

❌ Initial version - clean but opaque

function useCustomerInfo(customerId: string) {
  return useQuery(["customer", customerId], fetchCustomer)
}

This works. Until:

• You start seeing unnecessary refetches.
• You're not sure if caching is working.
• You're debugging why it hits the network on navigation.

✅ Updated version - self-explaining

function useCustomerInfo(customerId: string) {
  const queryKey = ["customer", customerId]

  const result = useQuery(queryKey, fetchCustomer)

  const debug = {
    queryKey,
    triggeredAt: new Date().toISOString(),
    cacheHit: !!result.data,
    requestId: crypto.randomUUID(),
    componentStack: new Error().stack,
  }

  if (import.meta.env.DEV) {
    console.groupCollapsed(`[useCustomerInfo] ${customerId}`)
    console.log(debug)
    console.groupEnd()
  }

  return { ...result, debug }
}

Now when something breaks or behaves unexpectedly:

• You see when the hook was triggered.
• You see whether the data came from cache.
• You can correlate request IDs with logs.
• You get stack info for tracing origin.

Component usage

const { data, debug } = useCustomerInfo(customerId)

useEffect(() => {
  if (import.meta.env.DEV) {
    console.log("Customer Info Debug:", debug)
  }
}, [debug])

This hook now documents itself at runtime.

Where I Draw the Line

✅ I use this in:

• Shared hooks
• Complex fetch logic with caching, retries, or fallbacks
• Logic used across features where breakage isn't obvious

❌ I avoid it in:

• Local-only UI state hooks
• Dumb stateless helpers
• Anything that introduces noise without clarity

The goal isn't to make code verbose. The goal is to make it explain itself when it matters.

It's More Than Code - It's a Posture

When you design traceable code, you:

• Stop thinking in return values only
• Start thinking in flows, triggers, and side-effects
• Build systems that age well and debug cleanly

🔚 Takeaway

Code that survives is code that narrates.

If you want to scale systems and avoid tribal knowledge rot, you need code that shows its flow, not just its output.

This isn't about overengineering. It's about engineering with empathy - for your future teammates, and your future self.

• Q. Ackermann Senior Engineer, Toolmaker, Systems Thinker GitHub | KodeReview | LinkedIn