Chain of Thought

Chain-of-thought prompting instructs a model to show its reasoning steps before arriving at an answer, which measurably improves accuracy on complex tasks like math, logic, and multi-step planning by forcing the model to work through a problem rather than pattern-match to a surface-level answer. For agent systems, visible reasoning also makes decisions transparent and debuggable: when an agent explains why it chose a particular tool or took a particular action, you can identify exactly where reasoning broke down rather than treating a failure as an opaque black box. Extended thinking features in models like Claude 3.7 Sonnet provide a dedicated reasoning space that does not count against output tokens, giving the model room to work through complex problems before committing to a response, which is especially valuable in multi-step tool-use loops where a wrong early decision compounds into larger mistakes.