Agentic CLEAR: Automating Multi-Level Evaluation of LLM Agents

Researchers introduced Agentic CLEAR, an automatic evaluation framework for LLM agents that produces dynamic, multi-level textual feedback.

The system analyzes behavior at system, trace, and node levels, giving developers both high-level and fine-grained insights.

Across multiple benchmarks and settings, Agentic CLEAR aligned well with human judgments and identified agent errors effectively.

It offers a scalable way to evaluate evolving agent tasks without depending on fixed, manual error taxonomies.