ReAct Agents

ReAct Agents are an advanced implementation within the Agentic AI paradigm, designed to synergize reasoning and acting for solving tasks in a structured, iterative manner.

The ReAct Agent operates in an iterative cycle:

  • Thought: The agent reasons through the current task and decides the next step.

  • Action: The agent executes an action, such as querying a database or calling an API.

  • Observation: The agent evaluates the result of its action to inform subsequent steps.

This cycle continues until the agent reaches a conclusion or determines that the task cannot be completed, at which point it communicates its findings or limitations.

ReAct Basic

ReAct Architecture Overview

ReAct Agents leverage a modular architecture that enables them to reason, plan, and act effectively. Below is a breakdown of their core components and workflows.

  • Agent Components:

    • Memory: The agent’s memory module stores past interactions and relevant data to inform future decisions.

    • Planning: This module breaks down complex tasks into manageable steps, helping the agent decide the sequence of actions.

    • Tools: The tools module enables the agent to interact with external systems, such as performing calculations, retrieving data, or running APIs.

  • System Prompt & User Prompt:

    • The System Prompt provides context and instructions for how the agent should operate within its environment, including the tools available.

    • The User Prompt is the input provided by the user, specifying the task or query the agent needs to handle.

  • LLM Interaction:

    • The Large Language Model (LLM) is at the core of the agent’s decision-making. It processes both the system and user prompts, and its outputs guide the agent’s actions. The LLM can reason about tasks, plan, and adapt based on external tool outputs.

  • External Environments:

    • The Local Environment consists of tools and systems the agent interacts with locally on its workstation.

    • The External Environment is any external system or API that the agent can call for additional information or actions.

This architecture allows the ReAct agent to efficiently break down complex tasks, take action, and observe the results in a structured, repeatable process.

ReAct Architecture

System Prompt

The system prompt plays a pivotal role in guiding the agent’s behavior. It provides the following:

  • Context: Establishes the environment (e.g., local or external) and the agent’s knowledge scope (e.g., knowledge cut-off date).

  • Instructions: Guides the agent on how to reason through tasks, use tools, and format responses.

  • Format: Specifies a structured response pattern, such as JSON, ensuring clarity and consistency in tool interactions.

ReAct System Prompt

Reasoning-Acting-Observing Cycle

The system prompt enforces a structured thought process:

  1. Thought: The agent reflects on the problem and decides which tool or action to use.

  2. Action: The agent performs the selected action.

  3. Observation: The agent evaluates the action’s result and determines the next step.

This structured loop ensures logical task progression and actionable outcomes.

ReAct Final

Exercise: ReAct Agents - Practical Example

Let’s see the Tool Calling and Routing Agents in Action!

From the agentic-workshop/lab-materials/04 folder, please open the notebook called 4.3-react-agents.ipynb and follow the instructions.

Advantages of ReAct Agents

  • Adaptability: Handles dynamic tasks by reasoning through unexpected scenarios and acting accordingly.

  • Efficiency: Uses tools only when necessary, reducing unnecessary computations or actions.

  • Transparency: Provides reasoning traces and structured outputs, making it easier to debug and improve agent performance.

ReAct Agents exemplify the core principles of Agentic AI by combining reasoning and acting in a way that mimics human problem-solving. Their structured approach ensures reliability, adaptability, and practical utility across diverse applications.