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AI code integrity

What is AI code integrity?

AI tools generate code faster than teams can review it. AI code integrity verifies that generated code is real, grounded, and safe to merge, before a reviewer spends time on phantom imports, hallucinated APIs, or placeholder logic.

AI code integrity is the discipline of verifying that code produced by AI coding agents is real, dependency-safe, and review-ready before it reaches merge.

It is not code review. It is not linting. It is not security scanning. It is the layer between agent output and human attention: the check that keeps fabricated agent output out of human review.

Install Community CLI Install Community CLI See defect patterns See defect patterns
  • No account
  • No telemetry
  • No source upload
  • Python · TypeScript · JavaScript · Go
  • --changed · --staged · --diff · --patch
  • JSON · SARIF
  • GitHub Actions
  • No source upload
$ shipmoor scan --changed ✗ Needs work - 2 of 2 findings block reviewpackage.json detected · 2 files · gate high⊘ blocks the gate  ·  ○ informational────────────────────────────────────────────────────────src/jobs/processor.ts · 1  ⊘ high    :12  phantom import  typescript.phantom_import    Module '@acme/workflows' is imported but not declared in package.json.    → Add '@acme/workflows' to dependencies, or remove the import.src/handlers/payment.ts · 1  ⊘ high    :47  placeholder implementation  typescript.placeholder.stub_handler    Handler returns HTTP 200 but body is a hardcoded stub.    → Implement the persistence step before merging, or remove the route.────────────────────────────────────────────────────────✗ gate fail · 2 high block at threshold "high"  exit 1→ fix the 2 blockers, then re-run  shipmoor scan --changed --fail-on high→ drill into one  shipmoor explain SHM-a12f9c84d501ee27

shipmoor scan --changed · two high-confidence findings from one agent run

Defining AI code integrity

AI code integrity means verifying that generated code is:

  • Real

    Imports resolve. Packages exist. Methods are implemented. Endpoints are defined.

  • Grounded

    The code implements the requested behavior, not a plausible-looking stub.

  • Dependency-safe

    Every external reference matches the actual manifest and installed environment.

  • Review-ready

    A human reviewer will not waste time invalidating fabricated APIs, phantom packages, or placeholder logic.

Integrity is about alignment between what the agent produced and what the codebase actually contains.

How agent-generated defects differ from human defects

Human developers make mistakes at the edge of understanding. AI agents make mistakes at the edge of context. When an agent cannot see the entire dependency graph, it infers one. When it cannot see an internal SDK surface, it constructs a plausible one. When asked to implement logic it cannot fully reason about, it produces a stub that looks complete.

  • Plausible Syntactically valid and often type-safe.
  • Systematic Recurring patterns across repositories and agents.
  • Contextual Tied to missing project-specific knowledge.
  • Expensive to review They require careful human inspection to invalidate.

Traditional tooling is not designed to catch these patterns.

The six characteristic AI code defect patterns

AI code integrity focuses on a small number of recurring failure modes. These are not random mistakes. They are structural artifacts of generative systems working with partial context.

  1. 01

    Phantom imports

    Imports of packages or modules that do not exist in the manifest or filesystem.

  2. 02

    Hallucinated API calls

    Method calls, client functions, or endpoints that are inferred but not implemented.

  3. 03

    Placeholder implementations

    Functions that return hardcoded success responses or TODO stubs instead of real logic.

  4. 04

    Stub paths

    Handlers that return 200 responses without executing required side effects.

  5. 05

    Swallowed errors

    Empty catch blocks or silent exception handlers that absorb failures.

  6. 06

    Suspicious tests

    Test suites that mock away real behavior or always pass without exercising the intended code path.

Why linters and PR review are the wrong layer

PR review is too late. Linting is too shallow. Integrity checks must run immediately after the agent finishes and before human review begins.

Layer What it checks What it misses
Linter Style, syntax, common anti-patterns Whether an import exists in the real dependency graph
Type checker Static type compatibility Calls hidden behind loose types, generated mocks, dynamic clients, or incomplete local type surfaces
SAST Known vulnerability signatures Placeholder business logic and fabricated internal APIs
PR review Human inspection after PR opens The cost of reviewing generated artifacts that should never have existed
AI code integrity check Generated-code grounding and dependency resolution None of the above

The timing problem

AI coding tools compress generation time. Review time does not compress at the same rate. Without integrity checks, reviewers are forced to:

Without integrity checks

  • Validate that imports resolve
  • Confirm that APIs exist
  • Detect silent stub implementations
  • Reconstruct intent from plausible but incorrect logic

With AI agent integrity

  1. Agent generates code.
  2. Integrity check validates grounding.
  3. Reviewer evaluates design and correctness, not fabrication.

AI code integrity restores the correct sequence.

How AI code integrity is enforced

An integrity workflow runs locally, immediately after the agent finishes. No account, no telemetry, no source upload. 

# After your AI agent finishes
$ shipmoor scan --changed

# Or scan staged files
$ shipmoor scan --staged

# Export machine-readable findings for CI
$ shipmoor scan --changed --format sarif > shipmoor.sarif

The integrity scanner:

  • Resolves imports against the actual manifest (package.json, requirements.txt, go.mod)
  • Verifies API calls against discovered code surfaces
  • Detects placeholder logic patterns
  • Identifies silent error handling constructs
  • Produces JSON or SARIF findings for CI

AI code integrity is not agent-specific

Teams increasingly use multiple AI coding tools. The defect patterns remain consistent across them. AI code integrity operates on the output, not the vendor.

  • Cursor
  • Claude Code
  • Codex
  • Copilot
  • Aider

From individual habit to team policy

AI code integrity begins as a developer habit and scales upward without changing the underlying checks.

01

Developer habit

  • Run a scan after agent output.
  • Fix findings before pushing.
02

Team policy

  • CI gates on high-severity integrity findings.
  • SARIF uploads provide review evidence.
  • Thresholds enforce consistency across repositories.
03

Organizational governance

  • Proof that AI-generated code was validated before merge.
  • Audit trails of findings.
  • Enforceable policy without blocking developer velocity.

AI code integrity, before review starts.

AI coding agents accelerate production. Integrity checks prevent fabricated artifacts from reaching human review. The goal is not to replace review. The goal is to ensure that review begins with real, grounded code.

Run your first integrity scan

Install the Community CLI. Local, no account, no telemetry.

curl -fsSL https://dl.shipmoor.dev/install-community-cli.sh | bash


Read the docs

AI code integrity questions FAQs

Short answers for developers and engineering leaders evaluating the category.

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AI code integrity for agent-assisted teams. Local-first. No account, no telemetry, no source upload.

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