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: https://arxiv.org/abs/2608.25593

Chat with Paper: https://academy.dair.ai/papers/jit-agent-a-model-that-writes-your-agent-harness-2608.25593