Original Summary

I've been building harness-router , an open-source decision layer for agentic/coding harnesses. The idea came from something that bothered me with agent loops: using a large model to reason about every single tool call feels wasteful. A lot of actions are obvious: known file → read it edit finished → run tests clear next action → just execute it So harness-router tries to keep that path cheap. When there is actual ambiguity, it exposes two MCP tools: route — uses Jev to choose between a small set of plausible actions. route_mcts — runs Monte Carlo Tree Search when the decision has consequences several steps ahead. The interesting part for me is MCTS. The current benchmark profile runs 4,096 simulations with depth 3 , while Jev is only used to seed the search. The rest of the simulations happen locally. I also recently benchmarked the router against normal Codex tool selection. The results are promising, although I consider them exploratory rather than a definitive benchmark — the MCTS budget was tuned on the same test cases. The project is still young, but I'm trying to explore a slightly different architecture for agents: LLM for open-ended reasoning. Small decision model for closed choices. Search when future actions matter. Nothing extra when the answer is obvious. It's written in Python, works as an MCP server, and is MIT licensed. Demo + benchmarks: https://harness-router.vercel.app GitHub is linked there as well. Would be especially interested in feedback from people building coding agents/harnesses — particularly what kind of routing benchmark you'd want to see next.   submitted by   /u/Revolutionary_Sir140 [link]   [comments]

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  • 发布时间：2026/9/27 04:40:10