A developer has built something genuinely useful - a system of eight nested validation gates that catches bad AI-generated code before it reaches production. Not through manual review, but through automated checks that enforce quality at every stage.
The system is built on a counterintuitive principle: spend 70% of your effort writing clear requirements, not reviewing code. Because here's what happens when you don't - the AI writes code that works, passes tests, and still solves the wrong problem entirely.
Requirements First, Code Second
The first gate is requirements validation. Before any code gets written, a separate AI agent reviews the requirements document. Not for spelling mistakes - for completeness, clarity, and whether the requirements actually match what the system needs to do.
This catches the most expensive mistakes early. A missing edge case in the requirements becomes a production bug three weeks later. An unclear acceptance criterion becomes a PR that gets rejected after two days of work. The validation agent asks: "Is this specific enough that any developer would build the same thing?"
If the answer is no, the requirements get rewritten before a single line of code exists.
Architecture as Guardrails
The second gate is architecture documentation. The AI coding agent doesn't just read the requirements - it documents its understanding of how the code will fit into the existing system. Database schemas, API contracts, integration points.
A human reviews this. Not the code - the architecture plan. Because this is where you catch the "technically correct but architecturally wrong" solutions. The AI might suggest a solution that works in isolation but breaks existing patterns, introduces new dependencies, or makes future changes harder.
Catching this before code exists saves days of refactoring.
Independent Validation
Here's where it gets interesting. The system uses separate validator agents - different AI models that weren't involved in writing the code. One agent writes, another reviews. No single AI judges its own work.
The validators check different things at different stages. Code quality validators look for maintainability, readability, adherence to project conventions. Security validators scan for vulnerabilities. Performance validators flag potential bottlenecks. Test validators ensure coverage is meaningful, not just high.
Each validator can reject code and send it back for revision. The coding agent doesn't move forward until all validators approve.
CI/CD as Enforcement
The final gates are automated pipelines. Unit tests, integration tests, end-to-end tests, security scans, performance benchmarks. Nothing gets merged without passing every check.
This isn't novel - most teams already have CI/CD. What's different is that the AI coding agent knows these checks exist and optimises for passing them. It writes tests alongside code. It runs security scans before submitting PRs. It checks for breaking changes in APIs.
The pipeline becomes a forcing function. If the code can't pass automated tests, it doesn't exist yet.
Long-Term Memory
The system includes memory of past mistakes. When a bug reaches production, the root cause gets logged. The next time similar code gets written, validators specifically check for that pattern.
This is the part that feels most like working with a human team. Experienced developers remember past incidents and check for similar issues instinctively. The validator agents do the same - they build up a checklist of "things that went wrong before" and verify each one doesn't happen again.
What This Actually Means
The system isn't perfect. It still produces code that needs human review. But it catches the obvious mistakes automatically, which means human reviewers can focus on the subtle stuff - business logic, user experience, edge cases that no automated check would catch.
The 70/30 split matters here. Most of the effort goes into defining what needs to be built, not checking if it was built correctly. Because if you get the requirements right, the code almost writes itself. And if you get the requirements wrong, no amount of code review fixes it.
For developers using AI coding tools, this is a practical template. You don't need eight agents - you need the principle. Validate requirements before code. Use independent reviewers. Automate quality checks. Remember past mistakes.
The goal isn't to eliminate human judgment. It's to eliminate the tedious parts so human judgment can focus on what matters.