Sumanth (@Sumanth_077) “Your AI agent doesn’t need a better model. It needs a better harness! JIT-Agent” — TopicDigg

Your AI agent doesn’t need a better model. It needs a better harness! JIT-Agent is a compact meta-agent that writes your agent harness on the fly. Most agent systems use a fixed scaffold for every task. The same planner, the same memory setup, the same tool orchestration, and the same execution strategy regardless of what the agent is actually trying to solve. JIT-Agent changes that by generating a task-specific harness across four core modules: memory, planning, action, and capability orchestration. So instead of treating the harness as static infrastructure, it becomes something the agent can compose based on the task itself. It can also improve that harness from execution traces and feedback without updating the generator model. This matters because a large part of agent performance comes from everything around the model. How context is stored, how the task is decomposed, which tools are available, when they are called, and how the execution loop is structured can change the final result significantly. The benchmarks make that pretty clear. With JIT-Agent, DeepSeek-V4-Flash outperformed GPT-5.6 by 9.1 points on DeepSearchQA and 4.3 points on OdysseyBench. GLM-5.2 also improved by up to 20.2 points with a better generated harness. Same model family. Better harness. Better agent. That is the part I find most interesting. As agent systems get more complex, improving the model may not always be the highest-leverage move. Improving the harness around it might matter just as much. 100% open source. I've shared the GitHub repo and paper in the replies!