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

Published: 1 week ago (January 7, 2026 at 08:22 PM EST)

6 min read

Source: Dev.to

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 1.7× more issues than human‑written code.

That figure comes from CodeRabbit’s analysis of 470 GitHub pull requests. The breakdown:

1.75× more logic and correctness errors
1.64× more code‑quality and maintainability issues
1.57× more security findings
1.42× 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 six months, I identified 13 ways they fail. Below are the top five.

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. Mid‑session the AI suggests “just a small change” to the interface. Three changes later, your frontend is broken, tests fail, and you’ve lost two 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. 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

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, but 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, Copilot, or any other model to write code, it:

Doesn’t know if your tests actually run
Can’t verify its changes didn’t break the build
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 SHA‑256‑hashed.

$ ./freeze-contracts.sh
✓ api/openapi.yaml: sha256:a1b2c3...
✓ db/schema.sql: sha256:d4e5f6...
Contracts frozen.

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.

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 → fakeToken
ACTION REQUIRED: Replace with real service calls.

This forces the “connect to real services” conversation early, when it’s still cheap to pivot.

Scope Limits

Hard stops at 5 files changed and 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.

This encourages incremental, deployable chunks instead of massive, risky changesets.

Deployability Rating Score (DRS)

A 0‑100 score that aggregates contract compliance, mock usage, scope adherence, test coverage, and static‑analysis findings. The framework blocks merges when DRS falls below a configurable threshold (default 85).

$ ./calculate-drs.sh
DRS: 78  ←  Blocked (threshold 85)
Reasons:
  • Contract violation (2)
  • Mock timeout (1)
  • Scope exceeded (1)

When the score is ≥ 85, the changes are considered safe to ship; otherwise, the framework aborts the CI pipeline and opens a ticket for remediation.

Takeaway

AI can boost productivity, but without hard, automated guardrails it also introduces a measurable increase in bugs, security flaws, and delivery instability. The AI Control Framework provides those guardrails by:

Freezing contracts and detecting drift in real time.
Timing out mocks to prevent accidental production leakage.
Limiting scope to keep changes small and reviewable.
Scoring deployability so only high‑confidence code reaches production.

Adopt the framework, or build something similar, and you’ll turn AI from a “suggestion engine” into a disciplined partner that respects the same quality standards you do.

Deployability Score (DRS)

The score is 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)

When DRS hits 85+, you know the code is production‑ready. No guessing.

Results

After implementing this framework across six 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

Research supports this approach:

44 % of developers who say AI degrades code quality blame context issues – Qodo
Microsoft reports it takes ~11 weeks for developers to fully realize AI productivity gains – LinearB
GitClear found code duplication increased 8× in 2024 – GitClear

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

The framework works with any AI assistant that can read files (Claude Code, Cursor, Copilot, Aider, etc.).

MIT licensed – LICENSE
100 % test coverage – 136/136 tests passing – GitHub

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 three 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:

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