Reasoning Agents Still Fail High-Risk Tasks

Making a model better at reasoning can make it worse at one of the most important production behaviors for agents: knowing when not to call a tool.

In an agentic system, a hallucination can become an action.

The model can invent a tool that does not exist, call a real tool for a job it cannot do, or use an irrelevant tool because the tool surface makes the model feel like it should act.

That is the reasoning trap.

You upgrade the model or turn on a reasoning mode because you want stronger planning, and you add chain-of-thought-style traces because the model seems more deliberate.

You fine-tune with tool-use rewards because benchmark numbers improve but then the agent becomes more confident about crossing tool boundaries it should respect.

SimpleToolHalluBench is a benchmark designed around this question:

When the required tool is missing, can the model abstain?

This is the exact behavior most production agents need before they touch real APIs, databases, browser sessions, payment systems, CRMs, ticketing systems, CI/CD pipelines, or code repositories.

SimpleToolHalluBench evaluates reasoning-enhanced models, reasoning distillation, toggleable thinking modes, and…