AgentRx: Systematic Debugging for AI Agents

AgentRx is presented as an open-source framework designed for systematic debugging of AI agent failures. The framework addresses the challenges associated with debugging AI agents, which often exhibit long, stochastic, and multi-agent trajectories. AgentRx aims to pinpoint the "critical failure step" in agent trajectories by synthesizing guarded, executable constraints from tool schemas and domain policies. The framework includes a trajectory normalization process, constraint synthesis, guarded evaluation, and LLM-based judging. AgentRx also introduces a benchmark consisting of 115 manually annotated failed trajectories across three domains. The article highlights the improvements in failure localization and root-cause attribution achieved by AgentRx compared to prompting baselines. The potential impact of AgentRx lies in its ability to improve the transparency and resilience of agentic systems. However, the effectiveness of the framework will depend on its ability to generalize across different agent architectures and domains, as well as the accuracy and reliability of the LLM-based judging component.

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