Verify AI Code Actually Works.
AI coding agents generate code that compiles, passes tests, and looks correct. But when you trace the actual data flow, entire pipelines are dead. We call this Silent Wiring — and we built the system to detect it.
The Origin Story
We built 1.1 million lines of production code with AI assistance. Our health checks said HEALTHY. Our tests passed. Our dashboards were green.
Then we built a behavioral verification system — one that doesn’t ask ‘did the code compile?’ but ‘did data actually flow through the expected path?’ The results were devastating.
3 completely silent data pipelines. A calibration engine returning hardcoded defaults for months. 82 runtime violations invisible to conventional testing. 1,218 evolution cycles with zero diversity — the same mutation type every time.
We call this pattern Silent Wiring: code that’s structurally connected but behaviorally dead. It passes every static check because the wiring exists. It fails every behavioral check because nothing actually flows.
This page explains the problem, the 3-layer architecture we built to solve it, and why conventional tools — tests, health checks, monitoring dashboards — cannot detect it.
SYMPTOMS OF INSUFFICIENT VERIFICATION
7 Problems. 3 Layers. Complete Verification.
These are the symptoms that emerge when AI-generated code lacks behavioral verification. Wiring Failures is the root — the rest follow.
Wiring Failures
"Code exists but isn't connected"
AI creates the class but forgets to register it. Data pipelines are structurally wired but behaviorally dead. The most dangerous problem because it's invisible to tests.
2Silent Failures
"100% of the time ignores failing tests"
Claims success when tests fail. Modifies tests to pass instead of fixing code. Trust-destroying behavior that compounds over time.
3Context Loss
"Dumber after compaction"
Every session starts from zero. The AI forgets architectural decisions, coding standards, and project knowledge across compactions.
4Guardrail Bypass
"I understood the rules but chose other behavior"
Instructions are in context but don't override trained patterns. CLAUDE.md gets ignored after 2–5 prompts.
5Quality Regression
"Cyclical bug-fixing loops"
Fix one thing, break another. The death spiral of AI-assisted development without behavioral verification.
6Incomplete Implementations
"First attempt will be 95% garbage"
Skips tests, wiring, edge cases. Marks task complete without running verification. Structurally present, functionally absent.
7Scope Creep
"Asked for one line, got 47 changed files"
Does more than asked. 'Helpful' refactoring that breaks working code and introduces new silent wiring.
THE 3-LAYER ARCHITECTURE
Topology → Liveness → Quality Gates
Three layers that work together to prove AI-generated code actually functions — not just compiles.
Declare Your Wiring
Define what should connect to what. Protocol/Implementation pairs, data flow paths, integration points. Make the expected architecture explicit and machine-checkable.
Every *Impl needs *Protocol. Zero violations in 1.1M lines.
Verify Continuously
Don't just check that code exists — verify that data actually flows through it. Behavioral probes that distinguish ACTIVE, STALE, and DEAD pipelines in real time.
3 silent pipelines found. 82 runtime violations caught.
Learn and Predict
Compound learning from every deployment. Pattern library that predicts which code is likely to become silently wired. Exit gates that block incomplete implementations.
57,000+ patterns. 112 issues fixed in one sprint.
THE PROOF
We Pointed It at Ourselves
We built CleanAim® using CleanAim®. Then we built behavioral verification and ran it against our own codebase. Here’s what it found.
WHAT MAKES THIS DIFFERENT
Why Tests and Monitoring Aren’t Enough
Tests Verify Structure
Unit tests confirm a function exists and returns expected output for given input. They cannot verify that real data actually reaches that function in production.
Health Checks Verify Availability
A health endpoint says the service is running. It says nothing about whether the data pipeline inside it is producing real results or returning hardcoded defaults.
Monitoring Verifies Metrics
Dashboards show request counts, latency, error rates. A pipeline returning hardcoded defaults has perfect metrics — zero errors, fast response, 100% uptime.
Behavioral Verification Verifies Flow
Silent Wiring detection asks the only question that matters: did data actually flow through the expected path and produce a real result? This is what CleanAim® adds.
Find Out If Your AI Code Is Silently Failing
Get a diagnostic of your AI-generated codebase. We’ll identify silent wiring, classify failure types, and give you a fix plan with a Silent Wiring Score.
Get a Silent Wiring Diagnostic