Tool-augmented reasoning is an approach in which a language model invokes external tools such as calculators, search or code execution to solve tasks beyond its parametric knowledge. The model interleaves reasoning steps with tool calls.
Semantic Classification
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- Tool-augmented reasoning lets a language model decide when to call external functions, pass arguments, and incorporate the returned results into its ongoing reasoning. Common tools include web search, calculators, code interpreters and database queries, which extend the model beyond what it memorised during training.
- By grounding intermediate steps in tool outputs, the approach improves factual accuracy and enables actions in the world, reducing reliance on potentially hallucinated content. It is a building block for agentic AI systems that plan and execute multi-step tasks.