A 2026 Sonar survey shows 88 % of developers say AI-generated code is swelling technical debt, and proponents of spec-driven development argue that a disciplined specification step can halt the drift.
Why the problem matters
When a human receives a vague ticket, they ask clarifying questions. An AI agent, by contrast, fills the blanks with its best guess and hands over code that looks plausible. The illusion of correctness is costly: the same Sonar poll reports that more than half of respondents have seen code pass basic checks yet hide subtle defects. Those defects pile up as technical debt, forcing later refactors, slowing feature delivery, and inflating maintenance budgets.
What spec-driven development looks like
Spec-driven development (SDD) flips the current order. Instead of prompting an AI model with a brief user story, the team writes a detailed, agent-executable specification that lives in the same version-control system as the code. The spec becomes the single source of truth—it records intent, edge cases, performance expectations, and any constraints an AI model must honor.
The process does not replace human design work; it codifies it. By moving decisions from a developer’s memory into a concrete document, both humans and future AI agents can trace why a piece of code behaves a certain way. Drafting a spec costs effort up front, but debugging ambiguous AI output costs far more later.
Changing the workflow
Product backlog – Keep items short, capturing only intent and high-level acceptance criteria. This list continues to drive prioritization.
Sprint planning – Teams discuss the overarching goal and agree on a Sprint Goal, but they hold off on detailed implementation until the spec is ready.
During the sprint – The person pulling the task writes a precise, machine-readable spec. The spec lists input formats, expected outputs, error handling, and any non-functional requirements. Because the spec is version-controlled, reviewers can comment, suggest edits, and approve changes just like code.
Definition of Done – Add “Spec reviewed and approved” to the quality gate. No code is complete until the spec passes the same review standards as the implementation.
Kanban adaptation – Insert two new columns: “Spec Drafted” and “Spec Approved.” Work items now flow from backlog → Sprint Goal → Spec Drafted → Spec Approved → In Progress → Done. This visual change makes the previously invisible coordination step explicit.
Tools that already enforce specs
Platforms such as GitHub Spec Kit and AWS Kiro have added gates that require a requirements document before any AI code generation starts. They don’t replace the AI model; they align literal-minded agents with human intent. By making the spec a prerequisite, these tools automate the shift without breaking existing CI/CD pipelines.
Potential pushback
Critics say writing a spec adds friction to an already fast-moving agile cadence. The counterpoint: the time spent drafting a spec is usually a fraction of the time later spent debugging AI-produced code born from an ambiguous prompt.
Another concern is that specifications can become outdated as requirements evolve. Version-control integration solves this: any change to the spec creates a new commit, triggers a review, and forces the team to re-evaluate the associated code. In practice, treating specs like code keeps documentation current.
What to watch next
Adoption is still early, but the momentum is visible. As AI code generators become more capable, the need for precise, machine-readable intent will only grow.
Bottom line: Turning vague prompts into concrete, reviewed specifications may feel like an extra step, but it converts guesswork into accountable decisions.