HARBOR: Automated Harness Optimization

📰 ArXiv cs.AI

Learn how HARBOR optimizes language model harnesses for better performance and why it matters for AI development

advanced Published 25 Apr 2026

Action Steps

Apply HARBOR to optimize harness configuration for a language model agent
Use automated configuration search to find the best harness design
Evaluate the performance of the optimized harness using metrics such as operational complexity and lines of code
Compare the results of HARBOR with manual harness optimization techniques
Integrate HARBOR with existing model deployment pipelines to streamline the optimization process

Who Needs to Know This

AI engineers and researchers can benefit from HARBOR to improve their language model agents' efficiency and scalability. This technology can also be useful for DevOps teams looking to optimize their model deployment pipelines.

Key Insight

💡 HARBOR treats harness design as a first-class machine-learning problem, enabling automated optimization of language model agents

Full Article

Title: HARBOR: Automated Harness Optimization

Abstract:
arXiv:2604.20938v1 Announce Type: cross Abstract: Long-horizon language-model agents are dominated, in lines of code and in operational complexity, not by their underlying model but by the harness that wraps it: context compaction, tool caching, semantic memory, trajectory reuse, speculative tool prediction, and the glue that binds the model to a sandboxed execution environment. We argue that harness design is a first-class machine-learning problem and that automated configuration search dominat

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