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AI Agents with LangChain & Autonomous Workflows · レッスン

複雑なワークフローの設計

複数のエージェント、ツール、意思決定ポイントを含む複雑な自律型ワークフローを設計します。

「複雑なワークフローの設計」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1/4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。

Beyond Simple Linear Chains

So far, we've built agents that follow a straightforward path. But real-world tasks are rarely simple!

Complex workflows allow your AI agents to handle intricate problems by combining multiple steps, making decisions, and coordinating different AI capabilities.

The Workflow Conductor

Think of a complex task like writing a research paper. It involves many steps:

  • Researching topics
  • Finding relevant sources
  • Summarizing information
  • Drafting sections
  • Reviewing and editing

Each step might need different tools or even different specialized agents. Orchestration is about making these steps work together seamlessly.

Building Blocks for Intricate Tasks

Designing complex workflows means thinking about how to combine:

  • Agents: Specialized for different sub-tasks.
  • Tools: External actions agents can perform.
  • LLMs: The brain for reasoning and generation.
  • Decision Points: Logic to choose the next step or agent.
  • Memory/State: How information flows between steps.

These elements are combined to create powerful, multi-stage processes.

Guiding the Workflow Path with Routers

A crucial part of complex workflows is the decision point, often implemented using a router.

A router analyzes the current input or intermediate result and decides which path the workflow should take next. This allows for dynamic, adaptive behavior.

For example, if a user asks for 'weather,' route to a weather tool. If they ask for 'news,' route to a news agent.

Router in Action: A Simple Example

A router directs your workflow by making decisions based on the input. Here's a simple Python function that acts as a conceptual router, directing queries to different 'handlers' based on keywords.

Try changing the input query to see how it routes!

def route_query_to_handler(query: str) -> str:
    """
    Simulates a router that directs a query to a specific handler
    based on its content.
    """
    query_lower = query.lower()

    if "weather" in query_lower:
        return f"Routing to Weather Tool for: '{query}'"
    elif "news" in query_lower:
        return f"Routing to News Agent for: '{query}'"
    elif "calculate" in query_lower or "math" in query_lower:
        return f"Routing to Calculator Tool for: '{query}'"
    else:
        return f"Routing to General LLM for: '{query}'"

# Main entry point for demonstration
if __name__ == "__main__":
    print(route_query_to_handler("What's the weather like today?"))
    print(route_query_to_handler("Tell me the latest tech news."))
    print(route_query_to_handler("Can you calculate 5 + 7?"))
    print(route_query_to_handler("Hello, how are you?"))

Keeping Context Across Steps

In a complex workflow, you often need to carry information from one step to the next. This is called managing state or context.

For example, an agent might extract key entities from a document, and then a subsequent step uses those entities to perform a web search.

LangChain helps by allowing you to define how outputs of one runnable become inputs for the next, often through dictionaries or specific input/output schemas.

Agents Working Together

Complex problems can be broken down into smaller tasks, each handled by a specialized agent.

  • An "Editor Agent" might refine text.
  • A "Data Agent" might retrieve information.
  • A "Planner Agent" might decide the overall sequence.

The workflow orchestrator ensures these agents receive the correct inputs and their outputs are correctly processed for the next stage.

Case Study: Research & Report Workflow

Let's design a workflow to generate a report on a given topic:

  1. Input: User provides a topic.
  2. Step 1 (Research Agent): Gathers relevant articles.
  3. Step 2 (Summarizer Agent): Processes findings, identifies key points.
  4. Step 3 (Drafting Agent): Writes an initial report draft.
  5. Step 4 (Reviewer Agent): Critiques the draft.
  6. Step 5 (Editor Agent): Applies improvements, finalizes report.
  7. Output: A polished report.

Mapping Out the Orchestration

When designing, it's helpful to visualize the flow. Imagine arrows connecting steps:

  • User Query → Research Agent
  • Research Agent Output → Summarizer Agent
  • Summarizer Agent Output → Drafting Agent
  • Drafting Agent Output → Reviewer Agent
  • Reviewer Agent Feedback + Drafting Agent Output → Editor Agent
  • Editor Agent Output → Final Report

Each arrow represents data flow and a potential decision point (implicitly, 'next step').

Workflow Decision Time

Consider a complex workflow designed to assist with customer support. It needs to either answer common FAQs or escalate to a human agent for complex issues.

Orchestrating Intelligence

You've learned how to think about designing complex AI agent workflows!

We covered the importance of orchestration, key components like routers for decision-making, managing state, and how multiple agents can collaborate on sophisticated tasks.

The ability to architect these intricate flows is key to building truly intelligent and autonomous applications.

よくある質問

「複雑なワークフローの設計」レッスンは無料ですか？

はい。「複雑なワークフローの設計」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。

「複雑なワークフローの設計」で何を学びますか？

複数のエージェント、ツール、意思決定ポイントを含む複雑な自律型ワークフローを設計します。 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1/4です。

「複雑なワークフローの設計」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. 複雑なワークフローの設計
  2. エージェントの非同期実行
  3. エラー処理とレジリエンス
  4. Human-in-the-Loop承認
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