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.
More from coding & agent
- Open-source agent harness runs 63-hour autonomous Riemann hypothesis attempt on a single RTX 3090 — GuiltyBookkeeper4849 · 2026-09-14
- Agentic coding brings back human connection at this game studio, founder says — TAbrodi · 2026-09-14
- Clarifying the AI agent messaging drama: it just couldn't access a link — Kyrannio · 2026-09-14
- Cloud queue + local agent hits a wall: laptop off, all scheduled jobs die — PriorElephant9 · 2026-09-14
- Freelancer's Gmail task-extraction script worked — until the manual 'last mile' killed it — Thefounderman1 · 2026-09-14
- smolvm v1.16 adds incremental checkpoints: git-like time travel for agents, 10x faster branching — LoganGrasby · 2026-09-14