Agent loop harness engineering - the orchestration layer that coordinates model calls, retrieval, and tool use - emerged as a more significant performance lever than model selection itself. In LangChain’s Terminal-Bench experiment, changing only the harness while keeping the model constant moved a coding agent from roughly 30th place into the top 5, (MarkTechPost). The finding aligns with separate Nvidia research demonstrating that fine-tuned agent harnesses enable strong performance even with weaker base models (TechCrunch). Separately, researchers at the UK AI Security Institute applied psychometric methods to popular safety benchmarks and found that these tests do not measure one consistent trait - blanket request blocking can artificially inflate safety scores while reducing model utility day-to-day (The Decoder).
A study from Princeton and UC San Diego examining AI agent “skills” found that skill libraries improve agent performance primarily through enforcing structured workflows rather than adding knowledge, but performance degrades as skill library size grows and agents struggle to locate the correct instruction set (The Decoder). Vercel and Ora launched Is Agentic, a free tool that audits website readiness for AI agents across 118 checks (MarkTechPost).