AI Architecture

AI Architecture

Design patterns for production AI systems in healthcare — inference serving, policy enforcement, and safe model deployment.

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5 articles in AI Architecture

Why Healthcare AI Needs a Clinical Foundation

Generic AI platforms operate on CRM contact data, not clinical data. Getting to genuinely clinical AI requires a foundation that includes patient identity resolution, care gap detection, and clinical context -- not an 18-month integration project on top of a generic platform.

11 min readFeb 27, 2026
AI agentshealthcare AIclinical AI foundation

Building an AI-Ready Platform: Why Most Software Will Break in the AI Era

Traditional enterprise software was built for human operators clicking buttons. AI agents need clean APIs, structured data, pre-computed intelligence, guardrails, and full audit trails. Most platforms will require fundamental re-architecture. Here is what AI-ready actually means.

14 min readFeb 23, 2026
AI-ready platformenterprise architectureAI agents

The Agent Boundary Problem: When AI Should Stop and Ask

Autonomous AI agents in healthcare need hard boundaries between capability and authorization. Human-in-the-loop is not a fallback — it is a design pattern that makes agents more useful, not less.

8 min readFeb 18, 2026
AI agentshuman-in-the-loopescalation policies

Tool Use Is Not Enough: Why AI Agents Need a Policy Layer

Function calling gives AI agents capability but not governance. A policy layer with pre-execution authorization, scope constraints, and action budgets is what separates a demo from a production system.

9 min readFeb 14, 2026
AI agentspolicy enforcementtool use

Why LLMs Should Never Directly Query Your Database

Direct database access from language models creates security, performance, and reliability risks. A serving layer with pre-computed metrics and policy enforcement is the production-grade alternative.

8 min readFeb 10, 2026
AI architectureLLM safetyinference serving

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