Tool-using LLM agents communicate their actions through language. Humans and downstream agents act on these claims, not on raw execution traces. We show that these claims are often misaligned with the actions: agents claim actions they never executed, and execute actions they never disclose. We formulate this as halluci-action. We introduce HalluciActionLens framework, which matches atomic action claims to recorded tool calls and audits every call for disclosure. It scores both directions as the…
Read moreTool-using LLM agents communicate their actions through language. Humans and downstream agents act on these claims, not on raw execution traces. We show that these claims are often misaligned with the actions: agents claim actions they never executed, and execute actions they never disclose. We formulate this as halluci-action. We introduce HalluciActionLens framework, which matches atomic action claims to recorded tool calls and audits every call for disclosure. It scores both directions as the Halluci-Action Rate (HAR), adds a severity variant for required and forbidden operations, and traces each misalignment to the step where it arises. Across five agents and 2,843 trajectories in an Odoo enterprise sandbox and AppWorld, every agent exhibits halluci-action despite explicit instructions to report each action, with HAR up to 45.9%.
Existing checks miss it: over half of factually clean or goal-complete trajectories contain a halluci-action, and 5--7% involve a required or forbidden operation. Agents' claims about their own actions cannot be taken at face value
