🤖 Can an AI agent build and improve the very execution scaffolding it runs on? Title: HarnessDev: Can LLMs Create and Evolve Their Own Agent Harness? URL: ❓ What exactly is a "harness"? 💡 It's the execution substrate around a model -- loops, tool use, context management, failure recovery, verification. Evaluations usually hold the harness fixed and only measure the model, but harness quality itself turns out to shape real capability a lot. ❓ Can LLMs build one from almost nothing? 💡 Starting from a deliberately weak seed, even the top creator (Opus 4.8) scored just 67.8 versus 86.2 for the human reference. The gap was widest in research/search tasks. ❓ Can models improve their own harness over time? 💡 All five self-runtime creators improved on visible feedback, but gains shrank sharply on held-out tasks. Of 64 version switches, only 2 showed improvement beyond noise. ❓ Does a harness transfer to a different executor model? 💡 Often not. Opus's harness dropped from 69.3 to 33.0 on SWE-Pro the moment the executor was fixed to Gemini -- harnesses bake in assumptions about the specific model running them. #AIAgents# #LLM#