DAIR.AI's Elvis Saravia: learning to build agent harnesses is the best bet for AI engineers

omarsar0 · x · 2026-09-14

Elvis Saravia (omarsar0, DAIR.AI) argues in a long thread that AI engineers should learn to build agent harnesses. Key points:

Building harnesses is one of the best ways to stay ahead: at minimum the knowledge transfers to tuning any harness (closed or open) you use; at best you apply domain expertise to build domain-specific harnesses that unlock real-world value and fix reliability issues big companies won't invest in.
Many YC builders are already doing this — deep work in a domain quickly reveals the opportunity, and harnesses are key to staying competitive in the agentic era while opening new product surfaces.
He counters the "models will generate harnesses on the fly" objection: there's no downside to learning.

The linked resource is his Harness Engineering paper collection (21 papers, from YC Paper Club): a harness is "everything between the model weights and the world" — the loop, context assembly, tools/skills, sub-agents, even the harness's own code. The list traces the field from a bare while-not-EOS loop in 2019 to self-rewriting harnesses in 2026, explaining why the same weights can score 30% or 95% on the same benchmark.

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DAIR.AI's Elvis Saravia: learning to build agent harnesses is the best bet for AI engineers

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