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  "text": "Agent harnesses are too restrictive.\n\nThat's because they're still designed as code.\n\nWhat if the harness itself were written in natural language and interpreted by an LLM at runtime?\n\nThis research explores the idea.\n\nThe work introduces Natural-Language Agent Harnesses (NLAHs), a structured natural-language representation that externalizes harness logic as a portable, executable artifact.\n\nInstead of scattering control flow across controller code, framework defaults, and tool adapters, NLAHs make contracts, roles, stage structure, state semantics, and failure taxonomies explicit and editable.\n\nAn Intelligent Harness Runtime (IHR) places an LLM inside the runtime loop to interpret and execute these harnesses directly.\n\nWhy does it matter?\n\nHarness design is increasingly decisive for agent performance, but it's buried in code that's hard to transfer, compare, or ablate.\n\nNLAHs make the orchestration layer a first-class scientific object.\n\nThe practical implication: harnesses become portable across runtimes, composable across tasks, and directly inspectable by humans and models alike.\n\nPaper: https://t.co/6itsSvh4Ag\n\nLearn to build effective AI agents in our academy: https://t.co/LRnpZN7L4c",
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