AI Agents with LangChain & Autonomous Workflows · 강의

복잡한 작업 흐름 설계

정교한 작업을 위해 여러 에이전트, 도구, 의사 결정 지점을 포함한 복잡한 자율 작업 흐름을 설계합니다.

복잡한 작업 흐름 설계은(는) CoddyKit의 무료 AI Agents with LangChain & Autonomous Workflows 강의입니다. 이것은 4개 중 1번째 강의입니다. 아래에서 전체 강의를 무료로 읽을 수 있으며, 내장 코드 에디터와 24/7 AI 튜터와 함께 브라우저에서 직접 실습할 수 있습니다. 이 강의는 AI Agents with LangChain & Autonomous Workflows 학습 경로의 일부이며, 진행 상황이 웹과 CoddyKit 앱에 동기화됩니다. AI Agents with LangChain & Autonomous Workflows 강의에는 총 4개의 강의가 포함되어 있습니다.

이 강의의 일부는 아직 번역되지 않았으며 영어로 표시됩니다.

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:

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

자주 묻는 질문

“복잡한 작업 흐름 설계” 강의는 무료인가요?

네 — “복잡한 작업 흐름 설계” 전체 내용을 이 웹사이트에서 무료로 읽을 수 있습니다. 인터랙티브하게 실습하려면(내장 코드 에디터와 24/7 AI 튜터), CoddyKit PRO로 업그레이드하면 AI Agents with LangChain & Autonomous Workflows 강의 전체를 잠금 해제할 수 있습니다. AI Agents with LangChain & Autonomous Workflows 강의에는 총 4개의 강의가 포함되어 있습니다.

“복잡한 작업 흐름 설계”에서 뭘 배우나요?

정교한 작업을 위해 여러 에이전트, 도구, 의사 결정 지점을 포함한 복잡한 자율 작업 흐름을 설계합니다. 브라우저에서 직접 실행하는 실습 코드로 AI Agents with LangChain & Autonomous Workflows을(를) 배우며, 24/7 AI 튜터가 강의를 진행하면서 질문에 답변해줍니다.

AI Agents with LangChain & Autonomous Workflows을(를) 시작하는 데 경험이 필요한가요?

사전 경험은 필요하지 않습니다. CoddyKit의 AI Agents with LangChain & Autonomous Workflows은(는) 초급자부터 고급 학습자까지를 위해 구성되어 있으므로, 여기서 시작하거나 처음부터 시작할 수 있으며 자신의 속도대로 진행할 수 있습니다. 이것은 4개 중 1번째 강의입니다.

“복잡한 작업 흐름 설계” 강의는 얼마나 걸리나요?

대부분의 CoddyKit 강의는 약 5~10분이 소요됩니다. 각 강의는 간결하고 인터랙티브하여 꾸준한 진행이 가능하며, 웹과 앱에서 중단한 부분부터 바로 시작할 수 있습니다.

이 AI Agents with LangChain & Autonomous Workflows 강의에서 코드를 작성하고 실행할 수 있나요?

네. 모든 AI Agents with LangChain & Autonomous Workflows 강의에는 내장 코드 에디터가 포함되어 있으므로, 브라우저에서 바로 실제 코드를 작성하고 실행한 후 즉시 AI 피드백을 받을 수 있습니다 — 로컬 설정이 필요 없습니다.

이 강의의 모든 강의

← AI Agents with LangChain & Autonomous Workflows(으)로 돌아가기