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Par l'équipe Semis · 10 oct. 2026
Comment planter des choux avec classe et style
Harness Engineering: How to Build AI Workflows That Never Fall Apart
Most people respond to a broken AI output by rewriting the prompt
Then they switch models
Then they upgrade to a larger context window
The model still forgets instructions
It still recommends the wrong tool
It still skips verification
It still claims success on broken work
The problem sits outside the model weights
The problem sits in the execution environment
That environment is the harness
Designing it is harness engineering
Dario Amodei explained this directly during the launch of Claude Code:
"Of course, you need an interface, you need a harness to use them"
The model acts as a reasoning engine
A model can predict the next logical step
It cannot build a stable operating system on its own
The harness controls what the model reads, what tools it can trigger, what data survives context resets, and when the execution must halt
Prompt engineering adjusts the wording
Harness engineering builds the infrastructure where that wording executes
Put a frontier model in an empty chat box, and it generates casual prose
Put that identical model in a repository with terminal access, automated test suites, project maps, and isolated workspaces, and it ships working software
The weights stay identical
The harness changes everything
OpenAI recorded the identical pattern when training agents on Codex
Their early runs failed because the environment was underspecified
The engineering team avoided asking the model to try harder
They built explicit capability checks and mechanical boundaries
When an agent fails repeatedly, stop editing adjectives in the system prompt
Inspect the operating system around the model
The Three Layers of a Reliable AI System
Builders often treat prompt chains, agent loops, and multi-agent graphs as competing ideas
They represent three distinct layers of one system
1. The Harness Layer (Environment and Memory)
The harness consists of everything existing outside model weights
It manages filesystem access, environment variables, authentication tokens, and durable files
Andrej Karpathy established the defining mental model:
The model is the CPU
The context window is the RAM