AI code stability
Working-looking code is not always change-ready.
AI-assisted development can produce a working-looking codebase quickly. The risk shows up later: when you need to explain the architecture, trace a feature, repair a seam, or make a cross-cutting change without asking the AI to rewrite half the project.
FoldEngine checks what holds, what fails, and what cannot be evaluated — then turns that into seed candidates, a proceed/hold/block decision receipt, and a governed next-step queue.
Before you delete three months of AI-generated code, run a Trial Probe.
What gets checked
AI-generated code stability assessment
The probe does not ask whether AI-generated code "looks good." It asks whether the evidence supports building on it, repairing it, migrating it, or stopping. It checks for codebase change readiness — the ability to safely absorb the next cross-cutting change.
Architecture traceability
Can the system be explained from its own structure? Or does understanding require re-asking the AI what it meant?
Generated-pattern drift
Are patterns the AI chose still coherent across the codebase? Or did successive prompts leave contradictory conventions?
Claim-to-artifact traceability
Are the joins between files, features, and intent traceable? Or does the code describe what it does without proving it?
Runtime and test proof
Do the test and runtime signals support the claims? Or are tests shallow, generated-but-untested, or missing entirely?
Change locality
Can you change one thing without rewriting everything around it? This is where AI code technical debt shows up first.
Stabilization debt
What seed candidates would make repair cheaper than rewrite? The governed queue orders next steps by what unlocks the most stability.
AI-assisted development risk
AI code maintainability: from pain to signal
These are real problems teams encounter after generating code with AI tools, vibe coding sessions, or AI-assisted prototyping. Each one maps to a signal the probe can check.
"I do not understand my own project"
Architecture traceability gap. The system cannot be explained from its own structure — you need the AI conversation history to understand what was built.
"The patterns were not mine"
Generated-pattern drift. The AI chose patterns session by session, leaving contradictory conventions, inconsistent abstractions, and structures nobody planned.
"Could not trace the logic"
Weak claim-to-artifact traceability. Files reference each other, but the reason chain from feature intent to implementation is not recoverable from the code itself.
"Had to reread everything"
Low navigability and poor change locality. A single feature change requires reading — and re-understanding — the entire project because context boundaries are missing.
"Needed to delete most of it"
High stabilization debt. The gap between the current state and a change-ready state is so large that rewrite feels easier than repair.
"It was simpler after rewriting from scratch"
Compression seed. The governed queue may recommend consolidation over extension — but only after the evidence shows what can be kept versus what should be dropped.
Key questions this page answers
Is your AI-generated code safe to change?
- Can we build on this AI-generated codebase? The probe checks whether the structure, tests, and invariants support extension — not just whether the code runs.
- Is this vibe-coded project maintainable? Vibe coding produces fast prototypes. The question is whether the result can absorb a cross-cutting change without a rewrite.
- What evidence shows this repo can safely change? FoldEngine checks layered invariants — what holds, what fails, and what cannot be evaluated from the available evidence.
- Are we about to rewrite code that could be stabilized? Seed candidates identify the smallest changes that would make repair cheaper than rewrite.
- Which gaps block a proceed decision? An evidence gap is an unevaluated invariant, not a footnote. Gaps determine whether the receipt says proceed, hold, or block.
What you receive
A governed decision package, not a generic code review
The result is not a list of findings. It is a decision package that tells you what the evidence allows you to do next.
Bounded findings
What the evidence supports — no claims beyond what was inspected.
Evidence gaps
What could not be evaluated — unevaluated invariants, not footnotes.
Seed candidates
Bounded change proposals that make the next move safer.
Proceed / hold / block decision receipt
A governed decision tied to evidence, not a subjective opinion.
Governed queue
Ordered next steps with unlock conditions — what to do first and why.
Stability Ledger summary
Private continuity record so the next probe starts from history, not from zero.
AI code needs checkpoints, not blame.
A software stability assessment that checks whether your AI-generated or AI-assisted codebase can safely carry the next change — before the rewrite crisis.