Mudit Singh is the Co-Founder and Head of Growth at TestMu AI, an AI-native unified enterprise test execution cloud platform.
I sit through many AI coding demos, and they mostly all end the same way. Someone types a sentence, and a working feature shows up 90 seconds later.
I’ve had close to a dozen conversations this year with CTOs and VPs of engineering who describe the same arc. They achieve fast, confident adoption and have a strong first quarter. Then, they slowly realize that speed and correctness aren’t the same thing.
That gap is why spec-driven development (SDD) exists. The conversation around it is stuck one step too early, fixed on writing a better spec and mostly silent on proving the code still matches it.
What SDD Actually Changes
Most SDD frameworks follow the same sequence: write requirements as user stories, convert those into EARS acceptance criteria, derive a plan and then let an agent touch code.
The EARS layer matters most for quality. “The system should handle failed logins gracefully” is a sentence people argue about for an hour. “When a user submits three consecutive failed login attempts, the system shall lock the account for 15 minutes” is a test case.
That’s the point of the syntax. A team that adopts SDD properly ends up with acceptance criteria that already read like testable assertions, most of the scaffolding a real test suite needs. Few teams treat it that way in practice, instead treating the spec as a coding artifact and the tests as a separate job for later.
The Part Every SDD Guide Skips
Most of what’s published on SDD stops at that point. It walks through writing the spec and then generating the code. Then, it goes quiet on the question that actually decides whether any of it worked: How do you know, today and every day after, that the generated code still matches the spec?
A well-written EARS requirement lowers the odds of drift, but it doesn’t remove it. I’ve watched an agent satisfy a requirement’s literal wording and still miss what it meant. A spec with 40 acceptance criteria and no way to check code against all 40 is a way to feel safe, not a safety net.
For a CTO or a board, undetected drift between spec and code shows up later as an audit finding, a compliance gap discovered during due diligence or a customer-facing outage traced back to a requirement nobody reverified. The cost is rarely visible at the moment it happens. It surfaces months later, in whatever incident forces someone to check the spec against the code and find they’ve quietly drifted apart.
Where Spec-Driven Testing Fits Into The Missing Verification Layer
This is where SDD and what I’d call spec-driven testing stop being separate disciplines. If the acceptance criteria are already test cases, something has to run them continuously against code that regenerates faster than any manual review cadence was built for.
Spec-driven testing uses those same criteria to generate and execute the verification layer, instead of an engineer hand-translating requirements into scripts. We’ve designed our testing agent to read a PRD or a doc directly and author tests from the intent behind it.
That keeps testing tied to the business outcome the spec was written for, not just its literal wording. The spec stops being a brief for the coding agent alone and becomes a brief for the testing agent, too. Once acceptance criteria are written as testable statements, the workflow that generates and runs the tests can also be automated.
What To Actually Do About It This Quarter
If you’re a CTO, VP of engineering or head of QA weighing whether to invest in this, here’s the practical version of my advice, not the theoretical one.
Put quality engineering in the room while the spec gets written, not after the code ships.
The single biggest predictor of whether SDD actually works for a team is whether QE has a seat at the table during spec review. Teams that write specs in an engineering vacuum and hand them to QA afterward end up with acceptance criteria that read well but don’t test well.
Pick one team and one workflow before mandating anything company-wide.
SDD has real overhead, and writing formal EARS criteria for a small feature or a prototype costs more than it returns. Prove the model on a team shipping something that has to survive years of maintenance before writing a policy about it.
Ask tooling vendors what happens when the spec changes six months from now.
Most SDD tooling is excellent at the first pass: writing the spec, generating the code, shipping the feature. Far fewer are built for the reality that specs get amended constantly, and someone needs to know whether last year’s code still honors this year’s requirements.
Treat spec staleness as a risk metric, not a documentation problem.
A spec nobody’s touched in four months while the code has changed weekly is worse than no spec at all. It manufactures false confidence for anyone who trusts it, including auditors and the board.
Budget for verification, not just generation.
Most AI tooling spend going into engineering budgets this year is aimed at writing code faster. Proving what was generated is actually correct gets less attention and less budget even though it’s where the real risk sits.
Bottom Line: The Honest Caveat
This isn’t a complete fix, and it shouldn’t be treated as one. It changes where judgment happens, earlier, at the spec, rather than removing the need for it. It addresses one kind of gap, not AI code quality as a whole. Broader concerns, such as security, system design and business context, still depend on people who understand more than the requirement in front of them.
Forbes Technology Council is an invitation-only community for world-class CIOs, CTOs and technology executives. Do I qualify?
