Jev as a New Primitive: Cheaper Scaling, Better Verifiers and Agent Harness Experiences
omarsar0 · x · 2026-09-20
omarsar0 shares early results integrating Jev into his custom agent harnesses, arguing the conversation shouldn't stop at "fast and cheap."
Key points:
- Complement, not competition: Jev should accelerate and extend existing test-time compute strategies, enabling new scaling approaches.
- Cheaper workflows: classification, control flow, deterministic steps, and large-scale labeling fit Jev better than a standard LLM, making harnesses easier to scale.
- Reliability: intelligent decision-making, structured intelligence for dynamic workflows, and new on-demand context management (tool calls, skill metadata).
- New agent experiences: dynamic UIs, efficient LLM councils, smarter routing/orchestration/planning, and more proactive agents.
- Evaluation: he sees big potential in LLM-as-a-Judge, agent verifiers, and synthesizing high-quality data to accelerate model-harness co-evolution (RSI-style).
A full guide is promised soon.
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