Structural Fixes vs Workarounds: When to Stop Patching and Start Rebuilding

Mar 27, 2026

I kept writing fixes. And they kept breaking.

Every time I patched one edge case, another one appeared. Attorney-represented sellers? Fixed. Title companies with 48-hour turnarounds? Fixed. Inspectors who reschedule twice? Fixed. Then came the inspectors who reschedule three times, and the title companies with 24-hour turnarounds, and the sellers with power of attorney…

That’s when I realized: I wasn’t fixing anything. I was building a tower of patches.

The Question That Changed Everything

Someone on r/ClaudeAI asked a question that hit me hard:

“How is your solution NOT also just a workaround? What’s the distinction?”

I had to stop and think. Was I solving problems or just hiding them?

The Diagnostic Framework

I started categorizing every “fix” I’d written into three buckets:

+------------------+----------------------------------------+----------------------------------+
| Type             | Definition                             | Signal                           |
+------------------+----------------------------------------+----------------------------------+
| Workaround       | Addresses symptom, breaks on new input | "It works for THIS case"         |
| Tool Switch      | Solves a different problem             | "Maybe we should use X instead" |
| Structural Fix   | Changes the process itself             | Edge cases resolve themselves   |
+------------------+----------------------------------------+----------------------------------+

The pattern was embarrassing. Nearly everything I’d written was in the first bucket.

What Workarounds Look Like

Here’s the pattern I kept repeating:

# Each edge case gets its own rule
def handle_transaction(transaction):
    if transaction.has_attorney_seller:
        return process_attorney_seller(transaction)
    if transaction.title_company_turnaround == 48:
        return fast_track_title(transaction)
    if transaction.inspector_rescheduled_count == 2:
        return reschedule_inspector(transaction)
    if transaction.has_power_of_attorney:
        return process_poA(transaction)
    if transaction.title_company_turnaround == 24:
        return ultra_fast_track_title(transaction)
    # ... 47 more rules ...

This is the workaround trap. Every rule solves one specific case. The code “works”—until a new case appears that doesn’t match any rule.

I was optimizing for “fixed” instead of “solved.”

The Structural Fix

After staring at my workaround tower, I noticed something. All these rules were trying to answer the same question: how much time pressure is this transaction under?

That’s when I restructured:

# One principle handles all cases
def handle_transaction(transaction):
    deadline_impact = calculate_deadline_impact(
        transaction.parties,
        transaction.constraints,
        transaction.timeline
    )
    return generate_action_plan(deadline_impact)

def calculate_deadline_impact(parties, constraints, timeline):
    """Why does a deadline matter, not just what the deadline is."""
    pressure_score = 0

    for constraint in constraints:
        # Tight turnaround? High pressure
        pressure_score += urgency_weight(constraint.deadline, timeline)

    for party in parties:
        # More parties = more coordination = more time needed
        pressure_score += coordination_weight(party)

    return pressure_score

The key insight: I stopped asking “what specific case is this?” and started asking “what principle governs this case?”

The Moment of Truth

A week later, a new case appeared: a seller with a court-appointed guardian, a title company with 12-hour turnarounds, and an inspector who reschedules every time it rains.

In my old codebase, this would have triggered three separate edge cases, each requiring a new rule.

In the new codebase? It worked. The principle-based logic calculated the pressure score, and the right action plan generated automatically.

Edge cases started resolving themselves. That’s when I knew I’d found a structural fix.

The Decision Tree

Now, before I write any “fix,” I run through this:

           Problem appears
                 |
                 v
    +------------------------+
    | Can I describe this as |
    | "it fails when X"?     |
    +------------------------+
           /          \
          /            \
        Yes            No
         |              |
         v              v
    +--------+    +-------------------+
    | It's a |    | Describe it as    |
    | symptom|    | "process fails to |
    |        |    | account for Y"    |
    +--------+    +-------------------+
         |              |
         v              v
    Workaround    Structural Fix
    (last resort) (preferred)

If I can describe the problem as “it fails when X,” I’m probably looking at a symptom. The real question is: what process created this failure?

When Workarounds Are Actually Okay

Let me be clear: workarounds aren’t always wrong. Sometimes you need to ship today. Sometimes the structural fix requires infrastructure changes you can’t make. Sometimes the edge case really is a one-off.

But here’s what I’ve learned: if you’re writing more than three rules for the same type of problem, you’ve probably found the symptom of a structural issue.

Edge case 1: Write a workaround
Edge case 2: Write another workaround
Edge case 3: STOP. You're working around a structural problem.

The Trap of “It Works Now”

The most dangerous phrase in debugging: “it works now.”

Every workaround works. That’s why they’re seductive. They give you the dopamine hit of “problem solved” without the hard work of understanding why the problem existed in the first place.

I now ask myself: “Will this work on the next case I haven’t seen yet?”

If the answer is “I don’t know” or “probably not,” I haven’t fixed anything. I’ve just delayed the problem.

A Heuristic for AI-Assisted Coding

With AI tools, this distinction becomes even more critical. AI can generate workarounds faster than any human. Give it a symptom, and it will give you ten patches before you finish your coffee.

But AI can also execute structural fixes consistently, which is where the real leverage is.

The question isn’t “can AI fix this?” The question is “what level of fix am I asking for?”

Level 1: "Fix this specific error"        -> Workaround (AI is great at this)
Level 2: "Fix this type of error"         -> Pattern fix (better)
Level 3: "Why does this error type exist?" -> Structural fix (best)

The Gap Analysis Method

Before implementing any fix, I now do this exercise:

List every “fix” I’ve attempted for this problem
Categorize each as workaround, tool switch, or structural
The gap between what I’ve tried and what I need reveals the real work

Problem: Misaligned ASCII diagrams

Attempts:
[x] Manual spacing adjustments     -> Workaround
[x] Fixed-width font requirement   -> Workaround
[x] Switched to Mermaid            -> Tool switch (different problem)
[ ] Coordinate-based rendering     -> Structural fix (NOT YET DONE)

The gap tells me what to build.

When Smart People Work Around a Problem

Here’s a pattern I’ve noticed: when smart people are all working around the same problem instead of solving it, that’s a signal. The problem is real, it’s unsolved, and the solution space is clear because you can see exactly where everyone stopped.

That’s where the real work lives.

The Maintenance Debt

Workarounds multiply. Structural fixes compound.

Workaround approach:
  Month 1: 3 rules
  Month 2: 7 rules (4 new edge cases)
  Month 3: 15 rules (8 new edge cases)
  Maintenance burden: GROWS over time

Structural approach:
  Month 1: Build principle-based logic
  Month 2: Handle 8 new edge cases automatically
  Month 3: Handle 20 new edge cases automatically
  Maintenance burden: SHRINKS over time

The time I invested in understanding the structure paid for itself within weeks.

Summary

Stop asking “how do I fix this?” and start asking “am I fixing the right thing?”

A structural fix changes the process. A workaround patches the output. If edge cases keep appearing after your “fix,” you’ve built a workaround. If edge cases start resolving themselves, you’ve solved the structure.

The three-rule test is your friend: after three workarounds for the same problem type, stop and look for the structural issue underneath.

Final Words + More Resources

My intention with this article was to help others share my knowledge and experience. If you want to contact me, you can contact by email: Email me

Here are also the most important links from this article along with some further resources that will help you in this scope:

👨‍💻 Reddit Discussion: ClaudeAI

Oh, and if you found these resources useful, don’t forget to support me by starring the repo on GitHub!