Chain of thought is a reasoning technique in which a language model generates intermediate reasoning steps explicitly, before producing a final answer, rather than jumping directly from question to conclusion.
Language models trained or prompted to produce chain-of-thought output effectively “think out loud” — breaking a problem into sub-steps, working through each one, and arriving at an answer that's grounded in the visible reasoning chain rather than pattern-matched in one leap. This measurably improves performance on tasks requiring multi-step logic, arithmetic, or planning, because the model can catch its own errors partway through rather than committing to a wrong answer immediately.
Chain of thought also has a practical transparency benefit distinct from its accuracy benefit: a visible reasoning trace lets a human or another system audit how the model arrived at a conclusion, which matters enormously for agentic systems making consequential decisions. It's not free, though — generating the intermediate steps costs additional tokens and latency, so it's applied selectively to problems complex enough to benefit from it rather than universally to every model call.
When a Neotask agent decides how to handle an ambiguous multi-step task, it produces an internal chain-of-thought trace weighing the available options before committing to a plan, and that trace is retained in the agent's session log so a reviewing human can see exactly why the agent chose the path it did.
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