Are We Giving AI Better Code - or Better Requirements? Specification Driven Software Development

AI coding assistants are getting remarkably good at turning instructions into working software.

But there’s a problem I’ve been thinking about:

What if the requirement itself is ambiguous or incomplete?

Are specifications becoming the missing interface between human intent and AI-generated software?

Faster implementation doesn't necessarily solve that problem. In fact, with AI agents capable of executing entire engineering tasks, we may simply produce the resulting rework much faster.

This is where Spec-Driven Development (SDD) gets interesting.

In my recent personal research projects, I’ve been experimenting with tools such as OpenSpec and Kiro, using structured, version-controlled specifications to guide AI-assisted implementation.

The emerging workflow looks less like:

Prompt → Code

and more like:

Intent → Requirements → Specs → Decisions → Tasks → AI Agents → Code → Evidence → Validate → Commit & Push → Archive Requirement

But I believe there is an even bigger opportunity: applying SDD before implementation, at the Requirements Engineering stage.

Could AI help identify ambiguity, decompose requirements, expose missing decisions, and establish traceability before an AI coding agent starts generating software?

AI agents can build faster. The real question is whether we can make sure they are building the right thing.

I explored this idea further in my latest blog post:

👉 From Requirements to Running Code: Can Spec-Driven Development Close the AI Software Delivery Gap?

Read the full article on Product Cognizant:
https://productcognizant.com/from-requirements-to-running-code-can-spec-driven-development-close-the-ai-software-delivery-gap/

I’d be interested to hear from others experimenting with SDD, Requirements Engineering, OpenSpec, Kiro, Spec Kit, Copilot, Claude, or agentic software development.

Are specifications becoming the missing interface between human intent and AI-generated software?