Missing Validations
This is the happy path pain point, and it's a close cousin of "Almost Correct Code." The AI is an optimist by default; it generates code assuming that all data will be clean, all users will behave, and all network calls will succeed. Without explicit, guardrail-enforced prompting, it will consistently fail to write the boring, defensive, pessimistic code—like input validation and error handling—that is absolutely essential for production-grade software.
AI models are trained to provide a functional solution for the most common use case. They are not trained to be defensive programmers. They generate code that implicitly trusts all inputs, ignores potential null or undefined values, and fails to wrap risky operations (like network calls or file I/O) in proper try...catch blocks or error handlers. This creates brittle, fragile code that works in a perfect scenario but shatters the moment it encounters the messy reality of production data and edge cases.
This is a primary driver of runtime errors, security vulnerabilities, and production incidents. A single missing validation check can lead to a Cross-Site Scripting (XSS) or SQL Injection attack (if an input isn't sanitized). A missing null check can cause a cannot read property 'name' of undefined error, crashing an entire service. This lack of defensive coding leads to an unstable application, erodes customer trust, and forces the engineering team into a constant, reactive state of bug-fixing instead of feature development.
The Trusting API Endpoint
An AI generates a POST /api/users endpoint that takes a JSON body and saves it directly to the database. It never validates that the email field is a valid email or that the password field meets complexity requirements, allowing corrupted data into the system.
The Silent Network Failure
An AI writes a fetch() request to an external API but forgets to add a .catch() block or check the HTTP status code. When the external API times out or returns a 500 error, the function fails silently, and the user is left with a spinning loader, forever.
The Happy Path Math
The AI generates a function calculateAverage(numbers). It works perfectly for [1, 2, 3], but it doesn't include a validation check for an empty array ([]) or a null input, causing it to crash the application with a "Division by Zero" error in production.
Missing Input Sanitization (XSS)
The AI generates code to "display a user's name on their profile." It takes user.name and renders it directly to the DOM, failing to sanitize the input first. An attacker signs up with the name <script>alert('XSS')</script>, which now executes on every profile page.
The problem isn't the AI; it's the lack of a human-in-the-loop verification and governance system. These workflows are the perfect antidote.
Security Guardrails
View workflow →The Pain Point It Solves
This workflow directly attacks the happy path problem by requiring security scans (SAST) before merge and enforcing security-focused code review checklists that check for missing input validation and sanitization. Instead of allowing AI to generate code that trusts all inputs, this workflow ensures that validation and error handling are caught before merge.
Why It Works
It enforces defensive coding. By requiring security scans (SAST) before merge, enforcing security-focused code review checklists that check for OWASP Top 10 vulnerabilities (including missing input validation and XSS), and requiring validation schemas for all user inputs, this workflow ensures that AI cannot generate happy path code that fails in production. This prevents runtime errors, security vulnerabilities, and production incidents from missing validation checks.
Release Readiness Runbook
View workflow →The Pain Point It Solves
This workflow addresses the "missing error handling" problem by running smoke tests covering code quality and security scans before the release window. Instead of allowing brittle, fragile code to reach production, this workflow ensures that missing validations and error handling are caught before release.
Why It Works
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Engineering Leader & AI Guardrails Leader. Creator of Engify.ai, helping teams operationalize AI through structured workflows and guardrails based on real production incidents.