ai-system-design

Architecting AI applications from the ground up, covering how components like models, retrieval, memory, and orchestration fit together into systems that hold up under real load. Content here digs into the design decisions and patterns that separate prototypes from production: handling failure modes, scaling agentic workflows, and avoiding the false assumptions that quietly break systems. Expect practical guidance on building reliable, maintainable AI architectures rather than abstract theory.

Balance scale comparing one large AI model against many small routed models in an agentic AI system

agentic-ai, ai-system-design, llm-safety, prompt-injection, ai-architecture, model-routing

Six Fallacies That Break Agentic AI Systems (And How to Design Around Them)

Six false assumptions break most agentic AI systems before they scale. This post names each fallacy, documents the real-world damage it causes, and outlines the design patterns that prevent it.