Chain of Thought
A prompting technique that instructs the model to break down complex problems into sequential reasoning steps before producing a final answer. Chain of thought significantly improves accuracy on math, logic, and multi-step tasks.
Chain of thought prompting works by encouraging the model to show its work rather than jumping directly to an answer. By generating intermediate reasoning steps, the model can handle problems that require multiple logical operations, comparisons, or calculations. This technique emerged from research showing that simply adding phrases like "let's think step by step" dramatically improved performance on benchmarks.
In agent systems, chain of thought is essential for reliable task decomposition and decision-making. When your agent needs to analyze customer data and decide which segment to target, chain-of-thought reasoning helps it weigh factors explicitly rather than making opaque jumps. For growth engineering, this translates to more predictable agent behavior and easier debugging. You can inspect the reasoning chain to understand why the agent chose a particular action, making it practical to deploy AI in high-stakes workflows like pricing decisions or user segmentation.
Related Terms
Model Context Protocol (MCP)
An open standard that defines how AI models connect to external tools, data sources, and services through a unified interface. MCP enables agents to dynamically discover and invoke capabilities without hardcoded integrations.
Tool Use
The ability of an AI model to invoke external functions, APIs, or services during a conversation to perform actions beyond text generation. Tool use transforms language models from passive responders into active problem solvers.
Function Calling
A model capability where the AI generates structured JSON arguments for predefined functions rather than free-form text. Function calling provides a reliable bridge between natural language understanding and programmatic execution.
Agentic Workflow
A multi-step process where an AI agent autonomously plans, executes, and iterates on tasks using tools, reasoning, and feedback loops. Agentic workflows go beyond single-turn interactions to accomplish complex goals.
ReAct Pattern
An agent architecture that interleaves Reasoning and Acting steps, where the model thinks about what to do next, takes an action, observes the result, and repeats. ReAct combines chain-of-thought reasoning with tool use in a unified loop.
Tree of Thought
An advanced reasoning framework where the model explores multiple solution paths simultaneously, evaluates each branch, and selects the most promising approach. Tree of thought enables more thorough problem-solving than linear chain-of-thought reasoning.