Harness engineering: the key discipline for building reliable AI agents
techNmak · x · 2026-09-26
techNmak argues that where AI agents are going depends on harness engineering—the runtime layer around the model managing context, tools, execution, state, checkpoints, limits and traces. As runs get longer, the harness must decide what stays in context, what is summarized or persisted, survive interruptions, enforce permissions, and preserve history for debugging. Many agent failures aren't fixed by switching models but by changing context selection, tool interfaces, or state handling. Harness engineering turns model capability into a controllable, inspectable, testable execution process.
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