Almost Correct Code
This is the most common challenge in AI-assisted development. The AI generates code that looks 95% right and passes a quick "happy path" review. The problem is that without AI-specific guardrails and verification workflows, this 'almost correct' code can easily be merged. This creates a false sense of velocity, as the plausible-looking code may still contain subtle bugs, unhandled edge cases, or 'stealth' security vulnerabilities that a standard code review process isn't designed to catch.
AI-assisted development tools are masters of the "happy path." They generate code that often works for the most common use cases, allowing it to pass initial functional tests. The danger lies in what's missing. The code is brittle, harboring subtle logic flaws, unhandled exceptions, performance bottlenecks, or insecure defaults that are invisible during a surface-level review. This creates a dangerous gap between "looks good" and "production-ready," leading to a false sense of velocity.
This "almost correct" code introduces massive technical debt and downstream costs. Engineering teams find their velocity crippled by time-consuming debugging sessions for production regressions that are notoriously hard to trace. Trust in AI tooling erodes, and the risk of shipping insecure or non-compliant code (e.g., code that violates PII or data handling policies) increases exponentially, directly impacting customer trust, system stability, and business reputation.
The Subtle Logic Bomb
An AI generates a function for calculating shipping discounts. It works for 9/10 scenarios but fails to correctly handle overlapping promotions or time-zone-based edge cases (e.g., a "Black Friday" sale ending at midnight UTC vs. EST). This leads to incorrect financial calculations and customer complaints.
The Insecure Default
A developer asks the AI to "create a file upload endpoint." The AI provides functional code but defaults to insecure permissions (777) on the upload directory and fails to sanitize filenames, creating a classic Path Traversal vulnerability that a security scanner might miss if not explicitly configured.
The "Happy Path" Test Suite
The AI generates unit tests that only cover expected inputs (e.g., a valid email string). It completely misses null inputs, empty strings, malformed data, or concurrency issues, giving the team a false sense of 100% test coverage while critical failure modes remain untested.
The Performance Bottleneck
An AI provides a "working" solution for processing a list of items. It uses an O(n²) algorithm (a nested loop). This passes the 10-item unit test but causes a production performance meltdown and database locks when a user hits it with a 10,000-item list.
The Deprecated API "Hallucination"
The AI, trained on an older dataset, confidently uses a deprecated library function that introduces a known memory leak. The code works during development but causes the production server to crash under load.
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.
TDD With Your AI Pair
View workflow →The Pain Point It Solves
This workflow directly attacks the "happy path" bias. Instead of asking the AI for a function, the developer first defines the contract by writing (or asking the AI to write) a comprehensive test suite that covers all known edge cases, failure modes, and security assertions.
Why It Works
It forces the developer and the AI to think about failure first. The AI's job is no longer "write this function" but "make these 15 failing tests pass." This reframes the entire development process around reliability and robustness from the start.
Release Readiness Runbook
View workflow →The Pain Point It Solves
This addresses the "insecure" or "non-compliant" code that looks functional. This workflow acts as an automated, AI-aware quality gate that integrates directly into the CI/CD pipeline.
Why It Works
It enforces automated governance. Before a pull request can be merged, the "Runbook" (e.g., a GitHub Action) triggers a series of automated checks. This could include SAST (Static Application Security Testing), dependency vulnerability scanning, performance/complexity analysis (e.g., cyclomatic complexity), and even custom AI-powered guardrails that check for things like "use of deprecated APIs" or "missing error handling for network calls." It ensures no AI-assisted change bypasses critical guardrails before release.
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Explore our workflows and guardrails to learn how teams address this issue.
Engineering Leader & AI Guardrails Leader. Creator of Engify.ai, helping teams operationalize AI through structured workflows and guardrails based on real production incidents.