arXiv:2605.26731v1 Announce Type: cross Abstract: A prevalent assumption in LLM agent deployment holds that more structured harnesses universally improve reliability, and that higher-capability models need proportionally less structural guidance -- together implying a monotone inverse relationship between model capability tier and optimal harness complexity. We test this hypothesis through a controlled 432-run experiment crossing six models across four capability tiers with three harness conditions (light, balanced, strict) on HEAT-24, a 24-task synthetic benchmark with git-based workspace ver
The proliferation of LLM agent deployments necessitates a deeper understanding of their sensitivities and optimal deployment strategies for reliability and performance.
This research challenges a fundamental assumption in LLM agent development by demonstrating that more capable models may not always require less structural guidance, impacting resource allocation and architectural design.
The understanding that optimal harness complexity for LLM agents is non-monotone across capability tiers, suggesting a more nuanced approach to agent design and deployment.
- · AI Agent developers
- · Open-source LLM communities
- · Enterprises deploying LLM agents
- · Companies relying on simplistic agent deployment assumptions
- · Developers neglecting empirical testing for agent harnesses
Further research into the specific conditions and model architectures that lead to non-monotone harness sensitivity.
Development of adaptive harnessing systems that dynamically adjust structural guidance based on agent capability and task complexity.
Increased adoption of rigorous empirical testing and meta-learning approaches in the design and deployment lifecycle of AI agents.
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Read at arXiv cs.CL