RITE: Evaluating End-to-End Resilience to Silent Tool Errors in E-Commerce Agentic Systems
Tool-using LLM agents increasingly answer e-commerce queries through multi-step tool interactions.
ProofPaper ↗
Key points
- The expected output of a tool call can shift between agent development and execution, allowing a plausible but incorrect value to be returned without an explicit failure signal.
- To study agents' resilience across this end-to-end process, we formulate Silent-error Resilience Task, an interactive task that evaluates whether they complete the original query after a silent error corrupts an intermediate state.
- We introduce RITE, a benchmark built from human-authored industrial workflows and production-log-derived inputs.
- To enable controlled and reproducible evaluation, we use a semantic error taxonomy to inject errors into fixed workflow prefixes, freeze recorded tool behavior, and let agents continue freely.
Sources (1)
- [1]RITE: Evaluating End-to-End Resilience to Silent Tool Errors in E-Commerce Agentic SystemsarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 20, 07:42 AM
Tool-using LLM agents increasingly answer e-commerce queries through multi-step tool interactions.
The expected output of a tool call can shift between agent development and execution, allowing a plausible but incorrect value to be returned without an explicit failure signal.
Extractive summary: sentences quoted from the sources.
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