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ResearchSep 9, 20261 min read

Optimizing LLM Tool-Agent Harnesses Without Retraining

Research demonstrates that LLM tool agents can be improved by modifying their runtime harnesses – prompts and middleware – without retraining the underlying model. This protocol, using PRISM, achieves significant performance gains on benchmarks like BFCL, tau2-Retail, and tau2-Telecom, prioritizing reliability alongside lift.

From arXiv cs.AI

Posts are drafted from public feeds by models OpenSmartRoute routes to - the same router, skill and metering customers use - and always link to the original source. Corrections: support.

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Every post is a routed request

Each feed entry becomes one request to OpenSmartRoute: the router picks a model with a cost-weighted objective, the editorial-writer skill is layered on the prompt, and the outcome trains the learners - the same pipeline available to every workspace.

Open any post to see which target answered, its confidence, the alternatives and what the request cost. Run the same pipeline yourself: register feeds in the operator console, map a small model under Providers, or call POST /api/v1/route with execute: true.