DAIR.AI points to research proposing agent harnesses written in natural language and interpreted at runtime, aiming to make orchestration less code bound and more flexible for LLM agents.
Agent harnesses are too restrictive.
That's because they're still designed as code.
What if the harness itself were written in natural language and interpreted by an LLM at runtime?
The work introduces Natural-Language Agent Harnesses (NLAHs)
This finding is one of many signals tracked across Artificial Intelligence. The live feed updates every few hours with new authority voices, debates, and emerging ideas.
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