Wiring Failures
"Your AI code passes every test. But is it actually working?"
— CleanAim® Engineering
The Problem
Your AI agent creates services, connects them to pipelines, registers them in the DI container. Tests pass. Health checks say HEALTHY. Code review looks clean. Everything appears to work.
But when you build a system that verifies behavioral correctness — not just structural correctness — you discover something alarming: some of those services are silently doing nothing. Data flows in, but nothing meaningful comes out. The pipeline is wired. It just doesn’t work.
We know this because we built 1.1 million lines of code with AI assistance. And when we pointed our own behavioral verification system at it, we found 3 completely silent data pipelines, 82 runtime violations, and a calibration engine that had been returning hardcoded defaults for three months.
The 4 Failure Types
After analyzing our own codebase and those of early design partners, we’ve classified wiring failures into four distinct types. Each one passes traditional testing. Each one is invisible to standard monitoring.
Dead Pipelines
Services connected by code, but no data ever flows through the connection. The pipeline exists. It’s registered. It’s structurally correct. But it processes zero records in production.
What we found: We found 3 data pipelines that were wired together but silently processed nothing. Integration tests passed because the pipeline contract was satisfied — with empty data.
Phantom Services
Registered in the DI container but unreachable from any application entry point. The service exists. It’s properly implemented. But no code path in the running application ever calls it.
What we found: Event handlers registered in the DI container that were never triggered in production. The events were dispatched to a different bus entirely.
Hardcoded Defaults
Functions that return static values instead of computing results from real inputs. The function signature is correct. The return type is correct. But the implementation shortcuts the actual computation.
What we found: A calibration engine returned hardcoded defaults for three months. Health checks said HEALTHY. Tests passed. It was computing nothing.
Stale Integrations
Connected to endpoints that have been deprecated, renamed, or silently disabled. The integration code exists and compiles. But the other end no longer responds meaningfully.
What we found: A monitoring service reported HEALTHY status for services that hadn’t responded in weeks. The health check tested the connection, not the behavior.
Why It Happens
AI agents optimize for the task in front of them: generate this service, write this function, create this test. They don’t optimize for system-level behavioral correctness — whether the pieces actually work together at runtime.
The problem is structural. Traditional testing verifies that code compiles, that interfaces match, and that unit tests pass. But none of this proves that data actually flows end-to-end through a live system. You can have 100% test coverage and still have dead pipelines.
Multi-file awareness is fundamentally limited. The AI might correctly implement a service and correctly write its tests, but have no awareness that the service registration happens in a file it never saw — or that a refactor two weeks ago changed the event bus the service depends on.
The Research
Research shows GPT-4 achieves 85.4% accuracy on function-level code generation — individual functions, isolated logic, self-contained algorithms. But when tested on class-level code with contextual dependencies — the wiring between services — accuracy drops to 62.5%.
AI is good at writing the parts. It’s significantly worse at connecting them. And the gap between 85.4% and 62.5% is exactly where silent wiring failures live.
What We Found
"Three data pipelines wired together that silently processed zero records. Every integration test passed because the pipeline contract was satisfied — just with empty data."
— CleanAim® internal audit
"A calibration engine that returned hardcoded defaults for three months. Health checks said HEALTHY. Tests passed. It was computing nothing."
— CleanAim® internal audit
"Event handlers registered in the DI container that were never triggered in production. The events were dispatched to a different bus entirely."
— CleanAim® internal audit
"A monitoring service that reported HEALTHY status for services that hadn’t responded in weeks. The health check tested the connection, not the behavior."
— CleanAim® internal audit
Why Existing Tools Miss This
Every major DevOps tool monitors something. None of them monitor whether your AI-generated code actually does what it claims to do at the behavioral level.
Datadog
What it does: Infrastructure observability — latency, throughput, error rates, APM traces, log aggregation. Recently added data quality monitoring for warehouse tables.
What it misses: Cannot verify that the content of data flowing through application pipelines is meaningful versus default values. Answers ‘is data moving?’ not ‘is this data correct?’
Checks that traffic moves on the highway. Can now monitor warehouse data quality. But can’t verify if application-level data is real or defaults.
SonarQube
What it does: Static analysis, code smells, vulnerability scanning
What it misses: Can’t verify that a registered service is actually reachable at runtime
Inspects the blueprint. Doesn’t check if the building has plumbing.
Pact
What it does: Consumer-driven contract testing between services
What it misses: Tests the contract shape, not whether data actually flows through it
Verifies the envelope has the right address. Doesn’t check if there’s a letter inside.
These tools solve real problems. But none of them answer the question Silent Wiring answers: Is this code actually doing what it’s supposed to do, end to end, right now?
THE SOLUTION
Silent Wiring Behavioral Verification
CleanAim®’s Silent Wiring system detects all four failure types through behavioral verification — proving that data actually flows, services actually compute, and integrations actually respond. Not by testing contracts. By observing behavior.
The 3-Layer Architecture — Topology Declarations, Continuous Behavioral Liveness, and Compound Learning — catches what tests, monitoring, and code review all miss. It’s the difference between checking that a pipe is connected and checking that water actually flows through it.
We know it works because we pointed it at ourselves first. It found 3 silent pipelines, 82 runtime violations, and a calibration engine that had been broken for months — in our own code. 112 issues fixed in one sprint.
The Evidence
Silent Wiring 3-Layer Architecture
Topology declarations, continuous behavioral liveness checks, and compound learning. Catches what tests, monitoring, and code review miss.
Find out what your AI code is silently not doing
Get a Silent Wiring Diagnostic. We’ll analyze your AI-assisted codebase for dead pipelines, phantom services, hardcoded defaults, and stale integrations.
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