Enji GuardAn AI code audit built for how AI actually fails
Guard audits AI-written code for the mistakes that look reasonable in a pull request but become expensive later: false confidence, missing context, weak tests, security gaps, and dependencies nobody chose.
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01
AI code often looks finished before it is proven
Generated code can be clean, readable, and still incomplete in the ways that matter. It may satisfy the prompt while missing the user workflow, the integration boundary, the permission rule, or the test that would prove the behavior under real conditions.
02
AI fails differently than people do
A human rarely imports a package that does not exist or writes a test that proves almost nothing on purpose. An AI can do both confidently because it is optimizing for a plausible next step. An AI code audit looks for that shape of risk, not only classic lint or SAST findings.
03
What makes the audit AI-native
Guard runs senior-engineer runbooks across failure modes common in AI-assisted development:
- Hallucinated, abandoned, or unreviewed dependencies
- Tests that pass while missing real user and integration flows
- Duplicated logic, invented abstractions, and local pattern drift
- Auth, access-control, and secret-handling gaps
- Repo context gaps that make the next agent change riskier
04
Hand-crafted runbooks, not pattern matching
The audits follow a source code audit methodology adapted for AI-assisted repos: what changed, what it depends on, what proves it, and what could break for users. The goal is signal developers trust enough to act on, not a wall of low-confidence alerts from another generic AI code audit tool.
05
Code and runtime together
Static analysis cannot see auth, rate limits, or user journeys. Guard reads the source and uses bounded runtime checks against linked URLs you authorize, so behavior that only appears in production is part of the audit too.
06
Findings become issues and pull requests
Findings come back as reviewable GitHub issues with evidence and rationale. When a fix is bounded and useful, Guard can open a narrow pull request for human review.
Quick questions
How is an AI code audit different from normal code review?
Review usually looks at a diff a human wrote. An AI code audit assumes the code was generated fast and confidently, and looks for the failure shapes that creates: invented dependencies, tests that prove little, and missing product context.
What makes this better than a generic code audit tool?
Generic tools pattern-match against fixed rules. Guard runs senior-engineer runbooks tuned to how AI actually fails, so you get findings worth acting on instead of a long, low-confidence list.
Is it a one-time audit or continuous?
Either. Run a single audit to see where you stand, or keep it recurring so the picture stays current as AI keeps changing the code.
Does it check the running app or only the source?
Both, when you link an approved app target. Static review cannot see auth, rate limits, or runtime behavior, so Guard adds bounded checks only inside the scope you authorize.
Audit your AI-written code.
Connect a repo and get your first audit report in a few minutes. No card.