Nate B. Jones defines an AI harness as the instructions, project files, memories, skills, tools, permissions and checks that shape a model before a user enters a prompt. He explains how repeated corrections can accumulate into an invisible system that makes newer models perform worse.
Nate B. Jones recommends mapping every control before changing it, identifying its owner and evidence, and distinguishing advisory text from enforceable locks. He also argues for one canonical home per rule so duplicated guidance cannot drift across skills and projects.
Nate B. Jones compares compact and heavily specified setups for Fable 5 and GPT-5.6. His broader lesson is to load specialist context only when the work reaches that phase, move machine-verifiable requirements into schemas or checks, and keep receipts that show which model, tools, fallbacks and validations actually ran.
Watch on YouTube
