arXiv:2606.12882v1 Announce Type: new Abstract: Large language models are increasingly deployed as agents for long-horizon tasks, yet their performance is shaped not only by model capability and environment design, but also by the harness that mediates agent--environment interaction. Existing harnesses are largely manually engineered, making them difficult to scale as trajectories grow longer and interactions become more complex. In this work, we ask whether harness can be generated by a learnable plug-in module that can be trained in an end-to-end fashion. We introduce HarnessBridge, a lightw
The increasing complexity and deployment of large language models as agents for long-horizon tasks necessitate more scalable and efficient agent-environment interaction mechanisms.
This work introduces a trainable, end-to-end module for managing LLM agent harnesses, promising to significantly improve the performance, adaptability, and scalability of AI agents in complex environments.
The reliance on manually engineered agent harnesses will decrease, replaced by learnable components that can adapt and optimize agent behavior autonomously, thereby accelerating agent development and deployment.
- · AI developers
- · Cloud computing providers
- · AI-powered service industries
- · Manual software engineering teams for agent interfaces
- · Companies with less sophisticated AI R&D
Increased efficiency and reliability of LLM agents across various applications.
Faster development cycles for complex AI systems, leading to a wider adoption of autonomous agents.
Enhanced automation of white-collar tasks, potentially collapsing existing SaaS layers and workflows.
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Read at arXiv cs.AI