arXiv:2605.22608v1 Announce Type: new Abstract: Agentic systems are becoming more capable: agents define strategies, take actions, and interact with different environments. This autonomy poses serious challenges for overseeing and assessing agent behavior. Most current tools are limited, focusing on observability with basic evaluation capabilities or imposing static, hand-crafted error taxonomies that cannot adapt to new domains. To address this gap, we present Agentic CLEAR, an automatic, dynamic, and easy-to-use evaluation framework. It produces textual insights into the agent behavior on th
The proliferation of increasingly capable LLM agents necessitates robust and dynamic evaluation frameworks to understand and control their behavior.
The ability to accurately evaluate and oversee AI agents is critical for their safe deployment and for collapsing complex workflows, directly impacting their commercial viability and societal integration.
This framework shifts agent evaluation from static, hand-crafted methods to dynamic, automated, and adaptable approaches, accelerating the development and reliability of autonomous systems.
- · AI agent developers
- · Enterprises adopting AI agents
- · AI research community
- · Cloud computing providers
- · Companies relying on static evaluation methods
- · Manual workflow management
- · Inefficient AI agent deployment processes
Improved understanding and control over AI agent behavior leads to faster development cycles.
More reliable and adaptable AI agents accelerate the automation of complex white-collar tasks.
The widespread deployment of highly capable AI agents could fundamentally reshape labor markets and industry structures.
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Read at arXiv cs.CL