Physicians Need to Learn the Harness, Not Just the Model
DeepSeek Harness shows why physicians must understand the context, tools, permissions, logs, and human checkpoints that turn an AI model into a working system.
DeepSeek Harness shows why physicians must understand the context, tools, permissions, logs, and human checkpoints that turn an AI model into a working system.
The AI race has been about model size for years. The real bottleneck is memory: trustworthy, auditable knowledge that persists across years, not conversations. Medicine will feel this shift first.
Prompting has a ceiling. Once you hit it, you are coordinating every step manually while the AI handles individual tasks. Here is the framework and three live workflows I use to cross that line.
For physician-developers, burnout often comes from low-value technical friction. The answer is not more endurance. It is better delegation to systems, automation, and agents.
A physician-developer's practical guide to giving an AI agent one bounded software assignment and requiring a plan, tests, review evidence, and a stopping condition.
Claude Code, Codex, Antigravity, OpenClaw, Hermes, and Omarchy illustrate a durable lesson for physicians: choose the agent's workplace before delegating the work.
AI agents do more than answer questions. This physician-developer guide explains the agent loop, levels of autonomy, and the checkpoints that keep humans accountable.
For physician-builders, agentic engineering is not prompt magic. It is bounded context, vertical slices, observability, and accountability.
Maternal-Fetal Medicine Specialist | Founder, CodeCraftMD | Atlanta Perinatal Associates