Behind the Scenes: Why We Had to Slow Down to Speed Up

“22 Skipped. 0 Created.”
“Team name is required and must be a non-empty string.”

This is what greeted us during a routine bulk upload for a volleyball tournament.

At first glance, it looked like a basic validation error. The frontend was trying to upload teams with no names. The backend was doing its job — rejecting any entry without a team_name.

But that’s not the full story.

This was a small architecture bug that scaled quickly, compounded by a misaligned AI response that nearly patched the problem in the worst way: by hardcoding logic where it didn’t belong. It’s a perfect example of what breaks when you move too fast, rely on brittle assumptions, and don’t pause to think systemically.

Let’s walk through it.

🧃 The Setup: “Grass Volleyball” Format

We were uploading team rosters using a format common to grass volleyball tournaments:

Charlie Podgorny, Nate Meyer, Peter Zurawski	1  
Grant Veldman, Will Mensching, Everett Haynes	2

No explicit team names — just player names and seeds.

On the frontend, the CSV parser detected this format and automatically created TeamInput objects like:

{
  team_name: '', // Will be generated from captain's last name
  players: 'Charlie Podgorny, Nate Meyer, Peter Zurawski',
  seed: 1
}

There was even a code comment explaining the plan:

// team_name: '', // Will be generated from captain's last name

The problem? That logic was never implemented.

🚨 The Breakdown

When the upload hit the backend, our validation function looked like this:

if (!team_name || team_name.trim().length === 0) {
  throw new ValidationError("Team name is required");
}

And so: every team was skipped with "Team name is required" errors.

We didn’t have a bug.
We had a breakdown in assumptions.

🤖 How the AI Almost Made It Worse

Here’s the original prompt I gave the AI:

“We should produce as much of what the backend expects on the frontend.
But don’t you agree that general engineering principles say deriving specific data from general input should happen on the backend?
Classic example: different frontend channels besides the website will force duplication if we don’t centralize it.”

And this was the AI’s first response:

“Let’s fix the frontend CSV parser.
Extract the captain’s last name and generate a team name like ‘Zurawski Team’.
Add fallback logic to handle duplicates.”

It was trying to help — but in the wrong layer.
Patching the frontend to generate team names was faster…
But it cemented the broken contract between systems.

I pushed back.

🧠 The Realization

I proposed a redesign:

“Should we separate the bulk upload into two steps — Parse and Submit?
Visually, this lets users preview and verify parsed data before it goes into the DB.
Programmatically, it gives the backend a clear contract: ‘Here’s the general data. You derive the specifics.’
As long as we have correct defaults and fallback logic, the backend should be responsible for generating the DB-safe values.”

This time, the AI aligned.

✅ “You raise excellent architectural points.
Separation of concerns. Single source of truth. Avoiding duplication.
Let’s implement a Parse → Submit flow:Frontend: parses raw CSV to ParsedTeamInput[], shows editable previewBackend: derives team names, applies fallbacks, validates, insertsUX: gives the human a chance to correct edge cases before they go live”

That was the unlock.

🧱 The New Architecture (What We Did Instead)

Step 1: Parse

• Frontend reads raw data, builds a minimal object with:
  • players
  • seed
  • optional hints (division, captain, etc.)
• No business logic
• Displays a preview table with auto-generated team names

Step 2: Submit

• Backend receives minimal input and:
  • Derives team_name from the captain’s last name
  • Applies fallbacks (e.g., "Team 1", "Team 2")
  • Validates full domain logic
  • Handles DB insertion and returns result breakdown

✍️ Why This Matters

This wasn't just a logic bug. It was a design trap:

• The frontend assumed the backend would fix things
• The backend assumed the frontend would never send invalid data
• The AI assumed the fastest patch was the right one

No one paused to ask: Should the frontend even be generating this?

🔄 When Speed Hurts, Slow Down

This is what it means to slow down to speed up:

• To not patch the frontend for a backend failure
• To not accept broken contracts between layers
• To not let your AI assistant “move fast” at the cost of long-term clarity

This was a real bug that exposed a brittle system boundary — and it almost got worse before we took a beat and redesigned it.

Would an agentic AI have caught this?
Maybe — if it had the training of a senior engineer.
But the real fix came from a human noticing the friction and zooming out.

💥 The Real Cost

This wasn’t just a missed detail — it was almost a wasted sprint.

If I hadn’t stepped back to notice the architectural drift,
I would’ve wasted two more weeks,
shipping a patch that silently duplicated logic,
broke the contract across clients,
and burned hundreds of dollars in AI tokens.

AI will move fast.
But someone still has to steer the system.