Harness Engineering
Harness engineering is introduced as a methodology for improving AI agent performance by curating the environment, context, and tools surrounding the model. It emphasizes embedding organizational requirements and feedback loops directly into the agent's operational framework.
Why it matters
As AI agents become more integrated into business workflows, this approach offers a structured way to ensure reliability, security, and organizational alignment.
“Most people do not know that they can just point their agents at my writing, tweets, podcasts, and talks and improve the output of their agents by 100x.”
Harness engineering, the practice of improving agent output by shaping the environment around it, holds a chosen model and coding agent constant as a black box. It improves the two external levers—context and tools—and curates the environment around them. The worker should be able to recover intent, operate the real system, respect authority, prove the outcome, and leave the next run better equipped.
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