arXiv:2606.17454v1 Announce Type: new Abstract: AI agent performance is not just a modeling problem, it is fundamentally a systems problem. The advanced capabilities of models are realized through agent harnesses. Therefore, a gap between model assumptions and harness behavior can easily prevent the model's full capabilities from translating into agent performance. We formalize this as the `intent-execution' gap: the mismatch between what the model intends and what the harness executes, and vice versa. We argue that minimizing this intent-execution gap is as important as other aspects of harne
The increasing sophistication and widespread deployment of AI agents highlight the practical challenges of translating theoretical model capabilities into real-world performance, prompting a deeper investigation into integration issues.
A strategic reader should care because this research identifies a critical bottleneck in AI agent performance, directly impacting the effective deployment and societal impact of advanced AI systems.
The focus expands from solely model improvement to include the crucial interaction between AI models and their operational harnesses, formalizing the 'intent-execution gap' as a key area for development.
- · AI agent developers
- · companies deploying AI agents
- · AI safety researchers
- · systems integrators
- · unoptimized AI agent systems
- · companies with poor AI integration strategies
Improved methodologies for evaluating and optimizing AI agent performance will emerge, leading to more robust and reliable AI applications.
Enhanced agent performance could accelerate the automation of complex tasks, impacting various industries and increasing productivity.
A clearer understanding of agentic failures due to intent-execution gaps could inform regulatory frameworks and ethical guidelines for autonomous systems.
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Read at arXiv cs.AI