Chain-of-Thought (CoT)

Chain-of-thought prompting asks the model to show its work — to reason through a problem step by step rather than jumping directly to an answer. This simple technique, first demonstrated by Google researchers in 2022, can improve accuracy on complex reasoning tasks by 50% or more.

The intuition is straightforward: when you solve a math problem, writing out intermediate steps helps you avoid mistakes. The same applies to LLMs. By generating reasoning tokens before the answer, the model effectively creates working memory and catches logical errors. Variants include "Let's think step by step" (zero-shot CoT) and providing worked examples (few-shot CoT).

For AI agents, chain-of-thought is not optional — it is essential. Complex tasks like debugging code, analyzing business requirements, or planning project architecture require multi-step reasoning. At Agentik {OS}, our agents are designed to reason explicitly before acting. A development agent does not just write code — it first analyzes the requirements, considers the existing codebase architecture, plans the implementation approach, then writes code. This structured reasoning produces dramatically better results.