Agentic Reasoning with Chain-of-Thought and ReAct
Agentic Reasoning with Chain-of-Thought and ReAct
For an AI system to be truly agentic, it needs more than just a large context window or access to tools; it requires a structured way to reason about those tools and its own thoughts. Two of the most influential frameworks for this are Chain-of-Thought (CoT) and ReAct.
1. Chain-of-Thought (CoT) Prompting
Chain-of-Thought (CoT) is the process of encouraging an LLM to “think out loud” by generating intermediate reasoning steps before arriving at a final answer.
Instead of jumping directly from Question → Answer, the model follows:
Question → Thought Process → Answer.
Why It Matters
CoT is particularly effective for multi-step reasoning tasks, such as math problems, logic puzzles, and complex architectural decisions, where a direct answer might overlook critical details.
2. The ReAct Framework
While CoT happens entirely “inside” the model’s parameters, ReAct (Reason + Act) connects reasoning with external actions.
A ReAct agent follows a cycle:
- Thought: The model plans what to do next.
- Action: The model executes a tool (e.g., searches a database, calls an API).
- Observation: The model reads the output of the action.
This loop continues until the agent has enough information to provide a final response.
Combining Reasoning and Action
In modern agentic systems, these frameworks are often combined. An agent might use CoT to break down a high-level goal into a series of ReAct steps, ensuring that every action is grounded in a logical plan.