AI Methodology: Using Encapsulation (deadend.dev)

AI-aided development can deliver big productivity gains but creates a new integration challenge: stochastic, hard-to-trust generated code. The proposed methodology reframes classic encapsulation for agent-driven coding: treat each AI-owned module as a black box defined by “fixed points” (explicit module interfaces/APIs and required side effects) and governed by “rules” (rigorous, in-process/unit tests). By constraining what the agent can change and mandating objective, dependency-free tests, you let the agent operate autonomously while preserving project-level understandability and control—an approach likened to imposing fixed points in a procedural “wave-function collapse” to channel stochastic outputs into coherent results. Practically, the workflow is simple: specify the module interface, provide concrete tests that validate intended behavior, hand full ownership to the agent, and accept any implementation that satisfies the tests. If a module later causes trouble, you add failing tests and iterate, escalate the task to a stronger model, or rip and replace the module—made feasible because modules are decoupled. This balances speed and autonomy with maintainability: humans keep domain knowledge and project control while delegating implementation detail to agents under deterministic constraints.