14 Ways AI Coding Agents Cut Corners — and Why Prompt Engineering Isn't Enough

I'm not a developer. I'm a factory auditor.
But I've been using Claude Code for a couple of months now, and I kept running into the same frustrating pattern. It would tell me "tests passed" when they hadn't. It would write code that looked right but broke at the edges. It would skip steps and claim everything was done.
So I started tracking. Every time Claude cut a corner, I wrote it down. After a few weeks, I had a list of 14 distinct patterns.
The 14 Patterns
Here's what I found. See if any of these sound familiar:
The Phantom Test — claims tests pass without actually running them
The Silent Skip — silently drops steps from the plan without explanation
The Shallow Fix — patches the symptom, ignores the root cause
The Hardcoded Escape — writes hardcoded values instead of proper logic
The Boundary Blind Spot — ignores edge cases entirely
The Scope Creep — adds unrelated features instead of finishing the task
The Placeholder Trap — leaves // TODO and pass in completed code
The Rewrite Reflex — rewrites working code instead of fixing the specific issue
The Assumption Leap — makes unfounded assumptions about data structures
The Error Swallower — catches exceptions silently without logging
The Config Drift — changes config files without updating documentation
The Memory Hole — forgets constraints from earlier in the conversation
The Optimistic Return — returns success without verifying the output
The Circular Fix — reintroduces the same bug it just fixed, two steps later
Why Prompt Engineering Falls Short
My first instinct was to fix this with better prompts. I tried everything:
"Be thorough."
"Don't skip any steps."
"Verify every output before moving on."
Long, detailed system prompts with explicit checklists.
It helped. A little. But the patterns kept resurfacing.
The problem is structural. LLMs have limited context windows, attention decay over long conversations, and a natural bias toward producing something rather than nothing. When the token budget gets tight, the model trades rigor for speed. You can't prompt your way out of that — the model doesn't even know it's doing it.
What Actually Worked
I built a structured workflow that doesn't rely on Claude being "careful." It forces Claude through a spec-first pipeline:
Spec phase — define exactly what success looks like before touching code
Build phase — implement with evidence at every step (file paths, line numbers, test results)
Verify phase — automated checks that the model can't skip or fake
The key insight: don't ask the AI to be honest. Structure the workflow so honesty is the only path.
Each phase produces concrete artifacts. The system doesn't ask "did you run the tests?" — it requires the test output file. It doesn't ask "did you check edge cases?" — it requires a checklist with explicit cases and results.
The Outcome
After implementing this, the corner-cutting dropped dramatically. Not to zero — LLMs are still LLMs — but the system catches it when it happens. The model can't claim "done" without evidence.
The surprising part: this actually made Claude faster overall. By eliminating the back-and-forth of "wait, did you actually test that?" and "why did you skip step 3?", the workflow cut my total task time by about 30%.
The Open-Source Version
I open-sourced this as a Claude Code skill called super-spec. It's not a product — it's the workflow I use daily, written as markdown instructions and bash scripts. If you're wrestling with the same AI reliability issues, it might save you some time.
I'd love to hear if others have noticed similar patterns, or if you've found different approaches that work. This feels like a problem the whole AI coding community is bumping into, and I suspect there are better solutions out there waiting to be discovered.
Have you noticed AI coding agents cutting corners? What patterns have you seen?