Tool-using LLM agents are increasingly handling e-commerce queries through multi-step tool interactions. A new paper from arXiv introduces RITE, an evaluation framework designed to test how resilient these agents are to silent tool errors—cases where a tool returns a value that seems plausible but is actually incorrect. The authors argue that the expected output of a tool call can change between development and execution, creating conditions for such errors to slip through.
RITE evaluates agents end-to-end rather than in isolation, meaning it captures how an agent behaves across an entire task when tools misbehave. This is particularly relevant for e-commerce, where a single wrong price, stock level, or shipping option could cascade into a poor user experience. The paper's framing suggests that current agent evaluations may not adequately account for these silent failures.
Because only the abstract was available for this digest, specific results and benchmark numbers are not reported here. The contribution, as described, is a methodology for systematically assessing resilience to a class of errors that is easy to overlook but potentially costly in real-world deployments.