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Steve Harlow
Steve Harlow

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Why Most AI Coding Sessions Fail (And How to Fix It)

#programming #productivity #ai #devops

The data behind AI-generated code quality—and a framework that enforces discipline

The Promise vs. Reality

AI coding assistants are everywhere. GitHub reports 15 million developers now use Copilot—a 400% increase in one year. Stack Overflow's 2024 survey found 63% of professional developers use AI in their workflow.

The productivity gains are real. Microsoft's research shows developers using Copilot achieve 26% higher productivity and code 55% faster in controlled tests.

But here's what the headlines don't tell you:

AI-generated code creates 1.7x more issues than human-written code.

That's from CodeRabbit's analysis of 470 GitHub pull requests. The breakdown:

  • 1.75x more logic and correctness errors
  • 1.64x more code quality and maintainability issues
  • 1.57x more security findings
  • 1.42x more performance problems

Google's 2024 DORA report found that increased AI use correlates with a 7.2% decrease in delivery stability.

And perhaps most damning: only 3.8% of developers report both low hallucination rates AND high confidence in shipping AI code without human review.

The Specific Failure Patterns

After tracking my own AI coding sessions for 6 months, I identified 13 specific ways they fail. Here are the top 5:

1. Mock Data That Never Dies

AI assistants love mock data. It makes demos look great and code compile cleanly.

The problem? Mocks survive to production.

In my logs, sessions where mock data existed past 30 minutes had an 84% chance of shipping with fake data still in place.

2. Interface Drift

You start with a clean API contract. Midway through the session, the AI suggests "just a small change" to the interface.

Three changes later, your frontend is broken, your tests fail, and you've lost 2 hours.

GitClear's 2025 research shows code churn—changes to recently written code—has increased dramatically since AI adoption, suggesting this pattern is widespread.

3. Scope Creep

"While I'm in here, let me also refactor this..."

What starts as a 50-line change becomes 500 lines across 15 files. Now nothing works, and you can't isolate what broke.

4. The "Almost Done" Trap

The AI reports the feature is "complete." Tests pass locally. You feel good.

Then you deploy, and it breaks immediately because:

  • Environment variables weren't configured
  • Error handling was added but never tested
  • A dependency was mocked that doesn't exist in production

5. Security Blind Spots

Studies show 48% of AI-generated code contains security vulnerabilities. Earlier GitHub Copilot research found 40% of generated programs had insecure code.

The AI writes syntactically correct code. It doesn't understand your threat model.

Why This Happens

The core issue isn't that AI is "bad at coding." It's that AI lacks accountability.

When you ask Claude or Copilot to write code:

  • It doesn't know if your tests actually run
  • It can't verify its changes didn't break the build
  • It assumes you'll catch the mocks, the drift, the scope creep

Prompt engineering helps, but prompts are suggestions. The AI can claim "I removed all mocks" while mocks still exist in your codebase.

You need enforcement, not suggestions.

The Framework Solution

I built the AI Control Framework to enforce discipline through external scripts—validators that check the actual state of your project, not what the AI claims.

Contract Freezing

At session start, interfaces (API specs, database schemas, type definitions) get SHA256-hashed.

$ ./freeze-contracts.sh
✓ api/openapi.yaml: sha256:a1b2c3...
✓ db/schema.sql: sha256:d4e5f6...
Contracts frozen.
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Any change during the session triggers an immediate alert:

$ ./check-contracts.sh
✗ CONTRACT VIOLATION: api/openapi.yaml changed
Hash expected: a1b2c3...
Hash found: x7y8z9...
STOP: Submit Contract Change Request or revert.
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This catches interface drift before it breaks your frontend.

30-Minute Mock Timeout

Mocks are allowed for the first 30 minutes—enough time to explore an approach.

After 30 minutes:

$ ./detect-mocks.sh
⚠ MOCK TIMEOUT: 2 mocks detected after 30-minute limit
- src/api/users.ts:42 → mockUserData
- src/services/auth.ts:18 → fakeTok...
ACTION REQUIRED: Replace with real service calls.
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This forces the "connect to real services" conversation early, when it's still cheap to pivot.

Scope Limits

Hard stops at 5 files changed, 200 lines added per session.

$ ./check-scope.sh
Files changed: 6/5 ✗
Lines added: 240/200 ✗
SCOPE EXCEEDED: Ship current work (if DRS ≥ 85) or revert.
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This forces incremental, deployable chunks instead of massive, risky changesets.

Deployability Rating Score (DRS)

A 0-100 score calculated from 13 components:

$ ./drs-calculate.sh
═══════════════════════════════════════
DEPLOYABILITY SCORE: 87/100
═══════════════════════════════════════
✓ Contract Integrity     (8/8)
✓ No Mocks               (8/8)
✓ Tests Passing          (7/7)
✓ Security Validation   (16/18)
✓ Error Handling         (4/4)
⚠ Prod Readiness        (12/15)

✅ READY TO DEPLOY (DRS ≥ 85)
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When DRS hits 85+, you know the code is production-ready. No guessing.

Results

After implementing this framework across 6 projects:

Metric Before After
Time to deploy 3-5 days 4-6 hours
Rework rate 67% 12%
Breaking changes per feature 4.2 0.3
"Works on my machine" incidents Weekly Rare

The framework doesn't slow you down. It prevents the 3-5 day rework cycles that happen when you deploy code that isn't ready.

Industry Context

The research supports this approach:

The problem isn't AI capability. It's discipline—and discipline requires enforcement.

Getting Started 

# Clone and install
git clone https://github.com/sgharlow/ai-control-framework.git
./ai-control-framework/install.sh /path/to/your/project

# Run your first DRS check
cd /path/to/your/project
./ai-framework/reference/bash/drs-calculate.sh
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The framework works with any AI assistant that can read files: Claude Code, Cursor, Copilot, Aider.

It's MIT licensed and has 100% test coverage (136/136 tests passing).

The Bottom Line

AI coding assistants are powerful. But power without discipline leads to:

  • Beautiful code that breaks on deploy
  • "Almost done" sessions that need 3 more days
  • Mock data that survives to production

Stop hoping AI code will work. Start knowing it will deploy.

Try the AI Control Framework →

Sources

All statistics in this article are sourced from:

  1. GitHub Blog - Research on Copilot Productivity
  2. CodeRabbit - State of AI vs Human Code Generation
  3. GitClear - AI Code Quality 2025
  4. Qodo - State of AI Code Quality
  5. Medium - Copilot's Impact on 15M Developers
  6. LinearB - Is GitHub Copilot Worth It?
  7. TechRadar - AI Code Security Issues

Have you struggled with AI coding assistant reliability? Let me know in the comments what patterns you've seen.

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