Many of the AI applications we interact with today are built on a streamlined, direct architecture: User → Prompt → LLM → Response That works surprisingly well for: chat assistants, summarization, content generation, and general productivity tooling. While this approach is incredibly effective for creative tasks and general productivity, high-stakes environments—where accuracy is non-negotiable—require a different level of structural support. In specialized fields like healthcare or finance, a probabilistic response isn't just a minor hurdle; it's a risk that needs to be managed through robust system design. I’ve spent the last few weeks exploring a decision-support architecture specifically tailored for these critical settings. The goal is to ensure that every output is grounded in fact, every recommendation is fully explainable, and every step of the reasoning process is auditable.…