The core theoretical framework posits that Human-AI collaborative software development functions under a specific set of constraints where AI agents act as probabilistic execution engines requiring high-fidelity context injection to minimize entropy accumulation in the codebase. The governing principle asserts that effective output generation is contingent upon establishing strict guardrails via pre-defined test cases, architectural documentation, and version-controlled baselines before delegating implementation tasks. This domain belongs to Human-AI collaborative engineering (specifically "vibe coding"), operating as a sub-discipline of software development methodology where the theoretical shift occurs from deterministic imperative programming to probabilistic prompt-based control structures that prioritize modularity and rigorous regression testing over monolithic feature generation.
The core theoretical framework posits that Human-AI collaborative software development functions under a specific set of constraints where AI agents act as probabilistic execution engines requiring h…