When large language models have access to tools, calculators, code execution, search, they tend to offload the hard thinking to those tools. The result is a correct answer with weak or incoherent reasoning behind it, a pattern researchers call Tool-Induced Myopia. It happens because models learn to treat tool outputs as the answer itself rather than as assisting evidence to reason from. This is especially important as AI systems become more agentic, since a system that can’t explain its decisions is harder to trust and debug.
