Last Tuesday your team merged a PR with 40 lines of clean TypeScript. Code review passed — the function was readable, typed correctly, and had a unit test. Twenty minutes after deploy, CloudWatch alerted: your Orders table was being fully scanned on every request. The merged function called .scan() without a partition key filter. Claude Code wrote it; nobody caught it because nobody — not the reviewer, not the tests, not the linter — had any way to know that Orders has 8 million items.
This is the gap infrawise check fills. It's a CI step that reads your actual DynamoDB schemas, PostgreSQL indexes, and query patterns, then fails the build when AI-generated code introduces anti-patterns against your real infrastructure.
The Gap Between Code Review and Infrastructure Reality
Static analysis tools like ESLint, TypeScript, and Semgrep analyze the code. They can't tell you that listAllOrders() is doing a full table scan, or that your PostgreSQL users table has no indexes and the new query will degrade from milliseconds to seconds as data grows.
AI assistants compound this. They write syntactically correct code that compiles and passes tests — but they're working from source files, not live infrastructure. They don't know your table's partition key distribution. They don't know you already have a GSI on status. They generate code against an imagined infrastructure and get it wrong in ways that only surface at scale.
