JIT-Agent Model for Dynamic Agent Harnesses
JIT-Agent enables developers to create competitive agent harnesses that outperform established systems like OpenCode and Claude Code.
JIT-Agent is a model that generates agent harnesses on the fly using a four-module protocol covering memory, planning, action protocol, and tool orchestration. It repairs harnesses mid-execution and self-evolves by distilling performance signals from prior configurations. When attached to DeepSeek-V4-Flash, it surpasses GPT-5.6 on DeepSearchQA (+9.1) and OdysseyBench (+4.3), while GLM-5.2 gains up to +20.2 points. The generated harnesses are performance-competitive with mature runtimes like OpenCode and Claude Code.
If you maintain a hand-built agent harness, this one is worth your time. (bookmark it) I feel like everyone is sleeping on the idea of dynamically generating agent harnesses on the fly. As you aim to own your harness, this is a topic more devs will lean into. Here is a great report discussing this topic. JIT-Agent is a model whose output is an agent harness. It formalizes the harness as a composable artifact under a fixed four-module protocol covering memory, planning, action protocol, and tool orchestration, then synthesizes one on the fly for any off-the-shelf agentic LLM. It also repairs harnesses mid-execution and self-evolves by distilling performance signals from an expanding archive of prior configurations. With JIT-Agent attached, DeepSeek-V4-Flash surpasses GPT-5.6 on DeepSearchQA (+9.1) and OdysseyBench (+4.3). GLM-5.2 gains up to +20.2 points. The generated harnesses are also performance-competitive with mature runtimes like OpenCode and Claude Code. Paper: arxiv.org/abs/2608.25593 Chat with Paper: academy.dair.ai/papers/jit-age… 💬 4 🔄 2 ❤️ 14 👀 1737 📊 8 ⚡
