JIT-Agent Synthesizes Adaptive Agent Harnesses on Demand

NUS researchers introduced JIT-Agent-27B, a model trained to synthesize adaptive agent harnesses on demand. By formalizing harnesses into memory, planning, action protocol and tool orchestration modules, it boosts off-the-shelf LLM performance across models and tasks.

2026-08-27 ~ 2026-08-28 · 3 related posts

• JIT-Agent: Improving LLMs via Just-in-Time Harness Evolution — NationalUniversityofSingapore · 2026-08-27
• JIT-Agent boosts performance via Just-in-Time Harness generation — 青稞AI · 2026-08-28
• JIT-Agent Dynamically Generates Harnesses to Boost Model Performance — omarsar0 · 2026-08-28