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AI Agents with LangChain & Autonomous Workflows · レッスン

エージェントの種類と意思決定

さまざまなエージェントの種類（例：ReAct、会話型）と、使用するツールを決定する仕組みを学びます。

「エージェントの種類と意思決定」はCoddyKit上の無料AI Agents with LangChain & Autonomous Workflowsレッスンです。 これはレッスン2/4です。 下記で完全なレッスンを無料で読むことができます。その後、ブラウザ内の組み込みコードエディタと24時間対応のAIチューターでハンズオン演習できます。 これはAI Agents with LangChain & Autonomous Workflows学習パスの一部であり、ウェブとCoddyKitアプリ全体で進捗が同期されます。 AI Agents with LangChain & Autonomous Workflowsコースには全4レッスンが含まれています。

このレッスンの一部はまだ翻訳されておらず、英語で表示されています。

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:

  1. Thought: The LLM reasons about the current situation and what to do next.
  2. Action: Based on the thought, the LLM chooses a tool and its input.
  3. 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!

よくある質問

「エージェントの種類と意思決定」レッスンは無料ですか？

はい。「エージェントの種類と意思決定」の完全なテキストはこのウェブで無料で読めます。インタラクティブに演習し（組み込みコードエディタと24時間対応のAIチューター）、AI Agents with LangChain & Autonomous Workflowsコースの残りをアンロックするには、CoddyKit PROにアップグレードしてください。 AI Agents with LangChain & Autonomous Workflowsコースには全4レッスンが含まれています。

「エージェントの種類と意思決定」で何を学びますか？

さまざまなエージェントの種類（例：ReAct、会話型）と、使用するツールを決定する仕組みを学びます。 ブラウザで直接実行するハンズオンコードでAI Agents with LangChain & Autonomous Workflowsを演習し、24時間対応のAIチューターがレッスンを進める中での質問に答えます。

AI Agents with LangChain & Autonomous Workflowsを始めるのに経験は必要ですか？

事前経験は必要ありません。CoddyKitのAI Agents with LangChain & Autonomous Workflowsは初級者から上級者向けに構成されているため、ここから始めるか最初から始めて、自分のペースで進むことができます。 これはレッスン2/4です。

「エージェントの種類と意思決定」レッスンにはどのくらい時間がかかりますか？

ほとんどのCoddyKitレッスンは約5～10分かかります。各レッスンはコンパクトでインタラクティブなので、着実に進歩し、ウェブとアプリ全体で正確に前回の場所から再開できます。

このAI Agents with LangChain & Autonomous Workflowsレッスンでコードを書いて実行できますか？

はい。すべてのAI Agents with LangChain & Autonomous Workflowsレッスンに組み込みコードエディタが含まれているため、ブラウザでリアルコードを書いて実行し、即座のAIフィードバックを取得できます。ローカル設定は不要です。

このコースのすべてのレッスン

  1. ツールの定義と利用
  2. エージェントの種類と意思決定
  3. 構築済みツールキットの活用
  4. エラー処理と安全なツール実行
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