Human-Certified Module Repositories for the AI Age

AI-generated code needs vetted building blocks: As large language models increasingly generate and assemble software components, the reliability of AI-constructed systems depends on having trustworthy, pre-certified modules available—similar to how human developers rely on trusted libraries, but with explicit human oversight built in.

Current software repositories pose supply-chain risks: The paper identifies that today's modular development ecosystems suffer from components with "unclear provenance, insufficient review, or unpredictable composition behavior," creating vulnerabilities that become amplified when AI systems automatically combine these modules without human judgment.

Human-Certified Module Repositories (HCMRs) combine human and automated oversight: The proposed framework uses both human review and automated analysis to certify modules, establish clear interface contracts, and track provenance—creating a curated ecosystem where AI agents can safely assemble code with predictable results.

Governance and accountability require new approaches: The paper emphasizes that reliable AI-constructed software systems need "foundational substrate for reliable and auditable" development, suggesting that responsibility for AI-generated code cannot rest solely with developers or AI systems, but requires institutional frameworks for certification and oversight.

Module composition behavior is unpredictable and risky: Beyond individual component quality, the paper highlights that how modules interact when combined by AI systems creates additional threat surfaces—a problem that requires explicit interface contracts and security review of component interactions, not just individual components.