Entity Binding Failures in Tool-Augmented Agents
2026-06-29 • Artificial Intelligence
Artificial Intelligence
AI summaryⓘ
The authors studied a problem where AI agents use tools correctly but still mess up by acting on the wrong real-world thing, like emailing the wrong person named Alex. They call these mistakes "entity binding failures" and show these errors are different from just picking the wrong tool. They tested several ways to fix this problem, such as checking which entity is meant before acting and asking for clarification when unsure. Their methods stopped these wrong-entity mistakes but sometimes held off completing tasks if things were unclear. The work shows that AI needs to correctly understand both which tool to use and which real-world entity to act on to be safe and reliable.
tool-augmented agentsentity binding failureAPI argumentsentity resolutionnatural languagedisambiguationprovenance trackingenterprise workflowstask completionsafety in AI
Authors
Rahul Suresh Babu, Shashank Indukuri
Abstract
Tool-augmented language-model agents are often evaluated by whether they select the correct tool, produce valid API arguments, and complete the requested task. However, an agent may choose the right tool and still act on the wrong external entity. For example, a request to "email Alex about the launch" may lead the agent to contact the wrong Alex, attach the wrong launch document, reply in the wrong thread, or update the wrong customer account. We call these errors entity binding failures. This paper studies entity binding failures as a distinct reliability and safety problem in tool-augmented agents. We formalize the separation between tool correctness and entity correctness, introduce a taxonomy of wrong-entity failures in enterprise workflows, and evaluate entity-aware execution mechanisms including entity-resolution preconditions, confidence-gated binding, clarification under ambiguity, and provenance tracking. In a controlled diagnostic evaluation across 60 tasks, five model backends, and six tool-use methods, all methods achieved 0.0 percent wrong-tool error, yet action-oriented baselines still produced wrong-entity actions in 24.0-26.0 percent of runs. Entity-aware methods eliminated wrong-entity actions and risk-weighted wrong-entity exposure in this setting, but reduced direct task completion by deferring under ambiguity. These findings show that safe tool use requires not only selecting the correct tool, but also reliably binding natural-language references to the correct real-world entity before action.