Tyler Slater

Tyler Slater

As Large Language Models (LLMs) transition from code completion tools to autonomous system architects, their impact on long-term software maintainability remains unquantified. While existing research benchmarks functional correctness (pass@k), this study presents the first empirical framework to measure "Architectural Erosion" and the accumulation of Technical Debt in AI-synthesized microservices. We conducted a comparative pilot study of three state-of-the-art models (GPT-5.1, Claude 4.5 Sonnet, and Llama 3 8B) by prompting them to implement a standardized Book Lending Microservice under strict Hexagonal Architecture constraints. Utilizing Abstract Syntax Tree (AST) parsing, we find that while proprietary models achieve high architectural conformance (0% violation rate for GPT-5.1), open-weights models exhibit critical divergence. Specifically, Llama 3 demonstrated an 80% Architectural Violation Rate, frequently bypassing interface adapters to create illegal circular dependencies between Domain and Infrastructure layers. Furthermore, we identified a phenomenon of "Implementation Laziness," where open-weights models generated 60% fewer Logical Lines of Code (LLOC) than their proprietary counterparts, effectively omitting complex business logic to satisfy token constraints. These findings suggest that without automated architectural linting, utilizing smaller open-weights models for system scaffolding accelerates the accumulation of structural technical debt.

Tyler Slater

Context: The integration of Large Language Models (LLMs) into core software systems is accelerating. However, existing software architecture patterns are static, while current safety assurance methods are not scalable, leaving systems vulnerable to novel adversarial threats. Objective: To design, implement, and evaluate a novel software architecture that enables an AI-driven system to autonomously and continuously adapt its own safety protocols at runtime. Method: We propose the Self-Improving Safety Framework (SISF), a runtime architecture that couples an unprotected, unaligned base LLM (mistralai/Mistral-7B-v0.1) with a dynamic feedback loop. This loop consists of an AI Adjudicator (GPT-4o) for breach detection and a Policy Synthesis Module (GPT-4 Turbo) that autonomously generates new, generalized safety policies (both heuristic and semantic) in response to failures. Results: We conducted a dynamic learning evaluation using the 520-prompt AdvBench dataset. The unprotected model was 100% vulnerable. Our SISF, starting from zero policies, demonstrated a clear learning curve: it detected 237 breaches, autonomously synthesized 234 new policies, and reduced the overall Attack Success Rate (ASR) to 45.58%. In a subsequent test on 520 benign prompts, the SISF achieved a 0.00% False Positive Rate (FPR), proving its ability to adapt without compromising user utility. Conclusion: An architectural approach to AI safety, based on the principles of self-adaptation, is a viable and effective strategy. Our framework demonstrates a practical path towards building more robust, resilient, and scalable AI-driven systems, shifting safety assurance from a static, pre-deployment activity to an automated, runtime process.