AI Agents with LangChain & Autonomous Workflows · Lesson

Agent Types and Decision Making

Explore different agent types (e.g., ReAct, conversational) and their underlying mechanisms for deciding which tool to use.

Agent Types and Decision Making is a free AI Agents with LangChain & Autonomous Workflows lesson on CoddyKit — lesson 2 of 4. You can read the complete lesson below for free — then practise it hands-on in the browser with a built-in code editor and a 24/7 AI tutor. It is part of the AI Agents with LangChain & Autonomous Workflows learning path, one of 4 lessons in the course, and your progress syncs across the web and the CoddyKit app.

Agent's Brain: How They Decide

Welcome! In this lesson, we'll dive into the fascinating world of how AI agents make decisions. It's what makes them seem 'smart' and capable of complex tasks.

Think of an agent as having a 'brain' that processes information and chooses the best next step from a set of options, often involving tools.

LLM: The Core Decision Maker

At the heart of most AI agents is a Large Language Model (LLM). The LLM isn't just for generating text; it's also the agent's primary decision-making engine.

It interprets your request.
It considers the available tools.
It generates a 'thought' process to decide what to do.

This 'thought' guides the agent's next action.

ReAct Agents: Reason & Act

One of the most common and powerful agent architectures is ReAct. This stands for Reasoning and Acting.

A ReAct agent works in a loop:

Thought: The LLM reasons about the current situation and what to do next.
Action: Based on the thought, the LLM chooses a tool and its input.
Observation: The tool executes, and its output is returned to the LLM.

This cycle repeats until the agent reaches a final answer.

ReAct in Action: A Simple Loop

Imagine you ask an agent: 'What is the current weather in London?'

Thought: 'The user wants weather info. I have a 'weather_tool'.'
Action: 'Call weather_tool with city='London'.'
Observation: 'Weather in London: 15°C, cloudy.'
Thought: 'I have the answer. I should respond to the user.'
Action: 'Respond: 'The weather in London is 15°C and cloudy'.'

This iterative process allows agents to tackle complex tasks step-by-step.

Code: Simulating Agent Decisions

This Python code simulates a simplified agent's decision process based on user input and available tools. Notice how it 'thinks' and decides on an 'action'.

def run_agent_cycle(user_input, available_tools):
    print(f"User Input: \"{user_input}\"")
    print("Agent's Thought Process:")

    if "search" in user_input.lower() and "web_search" in available_tools:
        thought = "User wants to search. Use 'web_search' tool."
        action = "web_search(query='LangChain agents')"
    elif "calculate" in user_input.lower() and "calculator" in available_tools:
        thought = "User wants calculation. Use 'calculator' tool."
        action = "calculator(expression='5+3')"
    else:
        thought = "No specific tool. Respond directly."
        action = "Respond: 'I can help with searches/calculations.'"

    print(f"  Thought: {thought}")
    print(f"  Action: {action}")
    print("-" * 20)

if __name__ == "__main__":
    print("--- Simulating Agent Decision Making ---")
    tools_available = ["web_search", "calculator"]

    run_agent_cycle("What is the capital of France?", tools_available)
    run_agent_cycle("Calculate 10 times 5.", tools_available)
    run_agent_cycle("Tell me a joke.", tools_available)

Conversational Agents: Remembering Context

While ReAct is powerful, some agents need to maintain a continuous conversation, remembering past interactions. These are conversational agents.

Their decision-making isn't just about the current turn; it's also heavily influenced by the conversation history (their 'memory').

How Conversational Agents Decide

For conversational agents, the LLM receives not only the user's latest input but also a summary or full transcript of the previous turns.

This memory helps the agent understand context.
It allows for follow-up questions and avoids repetition.
The decision to use a tool or generate a direct response is informed by the entire chat history.

We'll explore memory in detail in a later course!

Other Agent Architectures (Briefly)

Beyond ReAct and basic conversational agents, there are other sophisticated architectures:

Plan-and-Execute Agents: First create a multi-step plan, then execute it.
Self-Correction Agents: Evaluate their own outputs and try again if they detect errors.
Tree-of-Thought Agents: Explore multiple reasoning paths before committing to an action.

Each type offers different strengths for various complex tasks.

Factors Influencing Decisions

An agent's decision-making process is influenced by several key factors:

User Prompt: The clarity and specificity of the user's request.
Available Tools: The functions and capabilities the agent has access to.
Memory/Context: Past interactions that provide background.
LLM Capabilities: The model's reasoning abilities and knowledge.

Effective agent design means balancing these elements.

Quick Check: Agent Decision Types

Consider an agent designed to answer complex, multi-step questions that might require several tool calls, and also needs to maintain a consistent persona throughout a long conversation.

Recap: Agent Decision Making

Great job! You've learned how AI agents make decisions, moving beyond just text generation.

The LLM acts as the agent's 'brain', interpreting input and choosing actions.
ReAct agents use a 'Thought-Action-Observation' loop for step-by-step problem solving.
Conversational agents integrate memory to maintain context over time.
Various factors like prompts, tools, and memory influence an agent's choices.

Understanding these decision mechanisms is key to building powerful AI applications!

Frequently asked questions

Is the “Agent Types and Decision Making” lesson free?

Yes — the full text of “Agent Types and Decision Making” is free to read here on the web, and the AI Agents with LangChain & Autonomous Workflows course includes 4 lessons in total. To practise it interactively (a built-in code editor and a 24/7 AI tutor) and unlock the rest of the AI Agents with LangChain & Autonomous Workflows course, upgrade to CoddyKit PRO.

What will I learn in “Agent Types and Decision Making”?

Explore different agent types (e.g., ReAct, conversational) and their underlying mechanisms for deciding which tool to use. You practise AI Agents with LangChain & Autonomous Workflows with hands-on code you run directly in the browser, and a 24/7 AI tutor answers your questions as you work through the lesson.

Do I need any experience to start AI Agents with LangChain & Autonomous Workflows?

No prior experience is required. AI Agents with LangChain & Autonomous Workflows on CoddyKit is structured for beginners through advanced learners; this is — lesson 2 of 4, so you can start here or from the beginning and move at your own pace.

How long does the “Agent Types and Decision Making” lesson take?

Most CoddyKit lessons take about 5–10 minutes. Each one is bite-sized and interactive, so you make steady progress and pick up exactly where you left off across the web and the app.

Can I write and run code in this AI Agents with LangChain & Autonomous Workflows lesson?

Yes. Every AI Agents with LangChain & Autonomous Workflows lesson includes a built-in code editor, so you write and run real code right in your browser and get instant AI feedback — no local setup required.

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