When AI Makes Coding Faster, Specs Must Get Smarter

The Executable Spec Illusion

AI coding agents make software construction faster, which means software systems enter a more continuous state of change. Every new feature, rule, default, permission, workflow, or integration changes the expected behavior of the system.

Before AI, many teams treated incomplete specifications as a normal cost of delivery. Developers would discover gaps during implementation, ask follow-up questions, make judgment calls, or surface contradictions during testing. That process was slow and often wasteful, but the friction sometimes helped expose missing knowledge before the system changed too much.

AI changes that balance. When implementation gets faster, missing intent moves faster too. A vague or incomplete requirements spec can now become working code before Product Managers and QA engineers have fully checked whether the expected behavior is covered. A spec may look ready for AI-assisted development while still missing decision combinations, invalid states, default behavior, edge cases, or regressions against existing behavior.

This makes the upstream work more important, not less. Product Managers and QA engineers must now ask a sharper question: not "Do we have requirements ready?" but "Do these requirements cover the behavior obligations created by this change and protect the behavior that already exists?" They must make sure those specs cover the full set of behavior obligations introduced by ongoing product change.

The core problem is that most teams lack a systematic way to answer that question. They can read requirements. They can review requirements. They can add more requirements when something feels missing. But they often cannot reason from the decision structure of the change to the minimal set of behavior obligations the software must cover.

That is where the executable spec illusion becomes risky. The team sees concrete user stories and assumes the specification is complete. But the system may still contain untested combinations, implied defaults, missing negative cases, and silent changes to existing behavior.

When coding gets faster, incomplete executable specs become the new delivery risk.

Quality Becomes the Differentiator

When every development organization can produce software faster with AI, quality becomes the real competitive differentiator.

Speed will still matter, but it will matter less as a source of advantage. If most teams can generate code, tests, UI changes, API handlers, and migration scripts faster than before, the question changes. The winning teams will not be the ones that merely produce more software. They will be the ones that preserve correct behavior while the system changes faster.

Quality must be understood broadly. It is not only the absence of defects. It is the ability of the system to behave as expected, keep existing behavior stable, apply rules consistently, handle defaults correctly, reject invalid states, and make new changes without surprising users. A high-quality system is not just one that works today. It is one that can keep changing without losing its behavioral integrity.

Incomplete specs weaken that quality at the point where quality is cheapest to protect: before implementation starts. If a new feature introduces a new permission rule, default value, workflow state, or business exception, but the user stories do not cover it, the omission does not disappear. It simply moves downstream into design, code, tests, user manuals, and production behavior.

AI makes this more serious because it shortens the time between unclear intent and implemented behavior. A missing scenario can become missing code faster. A hidden contradiction can become inconsistent product behavior faster. A poorly defined default can spread across UI, API, database, and documentation before anyone has stopped to ask whether the behavior was fully specified.

The visible cost is rework. Teams must later fix missed cases, rewrite tests, update documentation, repair broken flows, and explain defects that should have been caught earlier. But the deeper cost is the loss of behavioral knowledge. The BDD suite stops being a reliable map of what the system should do and becomes a partial memory of what someone remembered to write down.

That loss compounds over time. Each change adds new behavior to a system that already has existing behavior. If PMs and QA engineers cannot see which decision axes changed, which rules must still hold, and which obligations need scenario coverage, the product becomes harder to reason about. The team may still move fast, but it moves with a weaker understanding of what must remain true.

In the AI era, quality depends on protecting behavioral knowledge before speed turns omissions into software.

Test the Specification Before Testing the Code

Start by converting requirements prose into Behaviour-Driven Development (BDD) scenarios that define positive paths, failure modes, and performance constraints. GenAI can draft the Gherkin syntax skeleton, then domain experts assert correctness. These scenarios become the ground truth for both test generation and future prompts.

BDD scenarios are executable examples, but examples do not automatically prove completeness. A scenario suite may look ready for AI-assisted development while still missing decision combinations, invalid states, default behavior, edge cases, or regressions against existing behavior.

Spec-level Property-Based Testing gives PMs and QA engineers a practical mental model for closing that gap.

Property-Based Testing is a testing approach where you do not only test selected examples. You define general rules that must always hold, then look for cases that break those rules. Instead of asking, "Does this example work?" you ask, "What must always be true, and can I find a counterexample?"

For BDD, we can use the same mental model one level higher. The system under test is not the running software. The system under test is the BDD specification itself. We are not yet asking whether the code behaves correctly. We are asking whether the scenarios fully describe the behavior that must be built, changed, rejected, or preserved.

The workflow starts with a change request. A change request may introduce a new feature, a new rule, a new default, a new role, a new field, a new workflow state, or a new exception. Each of these changes may alter what the system allows, shows, stores, calculates, prevents, or explains to users.

The first step is to extract the decision axes. A decision axis is a named, enumerable, behavior-relevant dimension of variation that, when changed, can alter what the system allows, shows, persists, or forbids for the case under specification.

Below is a simple example of decision axes for switching a light bulb:

In this example, the values on the Light bulb axis come from the business objects glossary, the values on the Switch axis come from the technical elements glossary, and the valid and invalid coordinate outcomes come from the key business rules.

The second step is to apply the rules. The axes define what can vary. The rules define what must hold. Together, they form a decision structure: a coordinate system of possible behavior. Some coordinates are valid. Some are invalid. Some require default behavior. Some apply only inside a declared scope. Some must be excluded because they would contradict the business rules.

The third step is to derive coverage obligations. A coverage obligation is not every possible coordinate in the decision structure. It is a minimal, rule-valid coordinate together with the observable behavior the specification must define. In plain words: it is a behavior case that must be exemplified by the BDD suite.

The light-bulb example makes this clear. The rules say that when the switch is Off, the bulb must be Dark, and when the switch is On, the bulb must be Light. The full grid has four cells, but only two valid behavior obligations: Off/Dark and On/Light. The other two cells are invalid states, not additional positive obligations.

Now map that back to BDD. Axes define what can vary. Rules define what must hold. Obligations define what must be exemplified. Scenarios show how the obligation is exemplified. A BDD scenario is one concrete assignment of values to the axes. It occupies a cell in the decision structure.

When we do decision-structure coverage, the question is: given the axes and the rules, which cells must exist for the specification to be complete? Those required cells are the coverage obligations.

• Axes are columns.
• Obligations are cells that should exist.
• Scenarios are cells that do exist.

This gives PMs and QA engineers a practical completeness question: given the axes and rules introduced by the change, which required cells must exist in the BDD suite? If a required cell has no scenario, the spec is incomplete. If two scenarios occupy equivalent preconditions but assert incompatible outcomes, the spec is contradictory. If a default is implied in one place and explicit in another, the spec is drifting.

Each coverage obligation should normally be surrounded by three kinds of scenarios. A positive scenario proves the expected behavior. A negative scenario rejects forbidden behavior near that obligation. An edge-case scenario shows the nearest relevant boundary, transition, omission, default, or state change.

For example, the Off/Dark obligation should not only have a positive scenario saying the bulb is dark when the switch is off. It should also reject the invalid case where the switch is off but the bulb is light. It should also cover the transition from on to off, where the bulb becomes dark. The On/Light obligation gets the same treatment in the opposite direction.

This is where the Property-Based Testing mental model becomes useful. Missing scenarios are counterexamples. Contradictions are falsified invariants. Weak edge coverage exposes an untested boundary. Terminology drift exposes a representation problem. Shrinking means finding the smallest missing or inconsistent scenario that reveals the gap.

Concretely, spec-level PBT checks five core properties of the BDD specification. These properties make "complete enough" explicit. Without them, the team leaves correctness implicit, and any reviewer, developer, or AI assistant must guess what the specification is supposed to protect.

Decision-Structure Coverage. For every required behavior obligation derived from the change request, the BDD suite must contain at least one scenario that asserts the correct behavior. In plain English: every required cell in the decision structure must be occupied by a scenario. If no scenario covers the obligation, the specification has a missing scenario or missing steps inside an existing scenario.

No Spec Contradictions. Two scenarios must not assert incompatible outcomes under equivalent preconditions. If two scenarios describe the same setup but expect different results, the problem is not implementation detail. The specification itself is inconsistent. This is a falsified invariant at the spec level.

Default Behavior Is Explicit and Stable. Whenever the change introduces or depends on a default, that default must be stated clearly and used consistently. For example, if the default priority is 2, the BDD suite should show how that default applies during create, inherit, migration, and API omission cases where relevant. If the default is explicit in one scenario, implied in another, and absent in a third, the specification is drifting.

Scope Isolation. New concepts introduced by the change must not leak outside their declared scope. If a new field, role, rule, permission, or UI concept applies only to one workflow, scenarios outside that workflow should not accidentally assert it. Absence matters here. At least one scenario should explicitly show that the concept does not apply where it must not apply.

Terminology and Representation Consistency. The same concept must be described with the same meaning, values, and labels everywhere. "Priority dropdown" and "priority control" may or may not mean the same thing. Numeric priority and labeled priority may or may not represent the same rule. "Administrator" and "non-administrator with edit rights" may or may not describe the same permission. If the language shifts, the specification accumulates ambiguity and long-term spec entropy.

These five properties turn review into falsification. Missing scenarios become counterexamples. Contradictions become broken invariants. Unstable defaults become drift. Scope leaks become invalid assertions. Terminology changes become signals that the team no longer has one shared representation of the behavior.

This is also where shrinking becomes useful. In classic PBT, shrinking means reducing a failing generated input to the smallest case that still fails. In spec-level PBT, shrinking means finding the smallest missing, contradictory, or ambiguous scenario that exposes the specification gap. The goal is not to produce more scenarios blindly. The goal is to find the smallest spec fix that restores behavioral coverage.

The hidden power of this method is learning, not just testing. PMs and QA engineers externalize domain knowledge by turning tacit rules into explicit properties. They prevent "it works, but nobody knows why." They also catch the kinds of errors that become more common when systems are changed quickly through refactoring, AI code generation, and rapid feature delivery.

The goal is not to create a huge number of scenarios. The goal is to create the smallest reliable set of scenarios that covers the behavior obligations introduced by the change and protects the behavior that already exists. That is the difference between adding examples and testing the specification for completeness.

Spec-level PBT turns BDD from a collection of examples into a disciplined search for missing behavior.

PMs and QA Become Guardians of Expected Behavior

PMs and QA engineers must move from writing examples to owning the full set of behavior obligations introduced by ongoing product change.

For Product Managers, this changes the shape of the job. A change request has never been just a description of a feature, a workflow, or a user need. It is a change to the behavior of a living software system. That means PMs must make the decision axes, business rules, defaults, invalid states, and scope boundaries visible before implementation starts.

For QA engineers, the shift is just as important. QA is no longer only about finding defects after software exists. In AI-assisted delivery, QA must also falsify the specification before code is written. The question becomes: what scenario is missing, what rule is contradicted, what default is implied but not stated, what behavior leaks outside scope, and what existing behavior might this change break?

This makes PMs and QA engineers more valuable. When AI coding agents increase implementation speed, the organization needs stronger upstream judgment. PMs protect product intent. QA protects behavioral completeness. Together, they make sure speed does not turn into unmanaged change.

Act now, and your team builds a reusable discipline for behavior coverage. Change requests become clearer. BDD specs become more trustworthy. Developers and AI agents receive better executable intent. Missing scenarios are found before they become missing behavior. Contradictions are resolved before they become inconsistent code.

Do nothing, and your team may still move faster, but with a weaker map of the system. BDD specs will exist, but they will act like scattered examples rather than reliable descriptions of expected behavior. AI will help produce more software, but it will also amplify omissions, ambiguity, and regression risk.

The trade-off is simple. Spec-level PBT adds work before implementation, but it removes confusion during and after implementation. It asks PMs and QA engineers to think harder about behavior earlier, so the whole organization spends less time repairing quality later.

In the AI era, PMs and QA protect quality by protecting the completeness of expected behavior.

Next Step

Before sending a change request into AI-assisted development, PMs and QA engineers should derive the decision axes, rules, and coverage obligations, then check whether the BDD scenarios fully cover them.