In the age of AI coding assistants like Cursor, Copilot, and Devin, Scrum Teams are experiencing a sugar rush of velocity. Features that used to take a Sprint are now being prototyped in days. The "vibe" is excellent.
But beneath the surface of this rapid progress, a silent crisis is brewing. We call it the "Vibe Gap", the distance between code that runs and code that is maintainable.
For Professional Scrum Teams, this poses a critical risk to the Increment. If developers stop writing syntax manually, they lose the intimate understanding of logic flows and variables. When the "vibe" breaks, when a bug appears in production, no one knows how to fix the "Black Box" code they generated.
Here is how AI creates hidden technical debt and how to update your Definition of Done (DoD) to survive it.
This article was originally published at ALDI 2026
The Three Pillars of AI Technical Debt
AI tools are incredible accelerators, but they are not architects. They predict the next token based on patterns, often prioritizing the "happy path" over long-term system health.
If you are a Scrum Master or Developer, look for these three types of "rot" in your codebase:
1. Dependency Bloat
AI doesn't care about your bundle size; it cares about solving the prompt. If you ask an agent to "parse a date," it might import a massive 2MB library like Moment.js instead of using a lightweight native function. This slows down the application performance, degrading the user experience over time.
2. Orphaned Logic
In the "vibe coding" flow, developers iterate rapidly, asking the AI to try Approach A, then B, then C. Often, the code for A and B is left behind, commented out or dead, cluttering the repository because the human developer didn't manually clean up the debris.
3. Security Hallucinations
This is the most dangerous risk. AI models are trained on public repositories, many of which contain insecure practices. An agent might confidentially generate code with hardcoded API keys or vulnerable SQL queries because "that's how it's usually done" in its training data.
The Failure of the "Gatekeeper" Model
In traditional software development, the Senior Engineer was the Gatekeeper. They reviewed every line of code in a Pull Request (PR) to ensure quality.
In the AI era, this model is collapsing.
A developer with an AI assistant might generate 500 lines of code in a single morning. A human reviewer cannot critically analyze that volume of logic in a reasonable timeframe. They will inevitably glaze over, assume the AI is correct, and click "Approve."
From Gatekeepers to Guardrails: Updating the Definition of Done
To maintain Technical Excellence, Scrum Teams must shift from Quality Assurance (checking at the end) to Quality Engineering (building checks into the process).
Your Definition of Done must evolve from manual reviews to automated Guardrails:
Static Analysis on Steroids: Tools like SonarQube must be configured to block builds, not just warn, when code complexity or duplication exceeds a threshold.
Secret Scanning: Implement pre-commit hooks that scan for API keys. If the AI hallucinates a credential, the code should never leave the developer's local machine.
AI-on-AI Review: Fight fire with fire. Use a separate, security-focused AI agent to review the code generated by the development agent.
Velocity Today, Outage Tomorrow?
If you don't implement these controls now, your increased velocity today will become your outage tomorrow. The goal of Professional Scrum is not just to build software faster, but to build software that lasts.
Join the Conversation - How do we maintain code quality when half the code is written by machines? We will be debating this at Agile Leadership Day India 2026 on February 28, 2026.
Join Engineering Leaders from across the globe as we define the standards for the Agentic Era.