GenAI has accelerated code production, but has made consistency (alignment) a bottleneck. Manual processes can no longer keep pace, and the architecture begins to fragment.
The problem does not manifest immediately — until the speed of change generation exceeds the organization’s ability to review them. Historically, control has relied on people: key experts in startups and review boards in enterprises. In both cases, there is a synchronous dependency on the center. With GenAI, the volume of changes is growing faster: code and prototypes appear within hours, while reviews remain slow. Teams are forced to choose between speed and consistency. Over time, this leads to architectural drift and complicates releases.
The solution shifts control from people to the system: declarative architecture. The essence is to capture architectural decisions in machine-readable declarations that can be automatically verified and applied. This is not about “best solutions,” but about “impossible to ignore.” The correct path becomes the path of least resistance. The trade-off is clear: upfront formalization and strict boundaries (bounded scope) are required for each declaration. Without boundaries, declarations degrade into the same documentation that no one reads.
The implementation is built around minimal units of behavior — vertical slices. The Event Model is translated into a formal schema (eventmodel.json), from which code artifacts are deterministically generated. Each slice is isolated: if there is an error, one slice is replaced rather than untangling a dependency graph. For cases where templates are insufficient, AI is used, but within a single slice — this reduces cognitive load and increases result stability. Iterations are automated through cycles with criteria checks (for example, Given-When-Then), where the agent retries until validation is passed. Simultaneously, the declarative approach extends to contracts: OpenAPI specifications become the source of truth, and linters and checks in CI/CD ensure compliance. Validation is built into IDEs and pipelines, so discrepancies are blocked before production.
The result is a shift of control “to the left” without increasing friction. Teams retain autonomy but operate within automatically verifiable constraints. Architectural consistency is achieved without the constant involvement of central roles. There are no explicit metrics in the source, but the qualitative effect is described: reduced cognitive load, predictability of releases, and the inability to deliver inconsistent changes to production. This appears as an evolutionary improvement: less manual oversight, more built-in constraints that the system applies itself.