AI Agents with LangChain & Autonomous Workflows · 강의

에이전트 사고 과정 디버깅

에이전트의 추론과 행동 순서에서 문제를 식별하고 해결하기 위한 체계적인 접근법을 적용합니다.

에이전트 사고 과정 디버깅은(는) CoddyKit의 무료 AI Agents with LangChain & Autonomous Workflows 강의입니다. 이것은 4개 중 2번째 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.

Debugging Agent Thoughts

Ever had an AI agent give a weird answer or get stuck? Debugging agents isn't like debugging regular code. Instead of just finding syntax errors, we need to understand the agent's "thought process".

This lesson will teach you how to peek into your agent's mind to see why it makes certain decisions and how to fix its reasoning.

The Agent's Inner Voice

An AI agent doesn't just output a final answer. Internally, it goes through a series of "thoughts". These thoughts involve:

Reasoning: What's the best next step?
Tool Selection: Which tool should I use?
Tool Input: What input should I give the tool?
Observation: What was the result of using the tool?

By examining this sequence, we can pinpoint where the agent's logic might be failing.

Common Agent Issues

Agents can fail in several ways beyond simple code bugs:

Wrong Tool: Selecting an irrelevant tool for the task.
Bad Tool Input: Providing incorrect or malformed input to a tool.
Reasoning Errors: Misinterpreting the problem or tool observations.
Infinite Loops: Getting stuck in a repetitive cycle of thoughts and actions.
Hallucinations: Making up facts or confidentially incorrect information.

Understanding these helps you know what to look for.

Seeing Agent Steps with Verbose

LangChain provides a simple way to see an agent's internal steps: the verbose=True parameter. When you set this, the agent will print its entire thought process to the console as it executes.

This "log" includes every Thought, Action, Action Input, and Observation, giving you a complete picture of its decision-making journey.

Tracing a Basic Agent

Let's see verbose=True in action. This agent uses a simple tool to get information. Pay attention to the output in the console!

Note: This code requires an OpenAI API key. Set OPENAI_API_KEY as an environment variable or uncomment and replace "YOUR_KEY".

import os
from langchain_openai import ChatOpenAI
from langchain.agents import AgentExecutor, create_react_agent, Tool
from langchain import hub # For standard prompts

# 1. Define a simple tool
def get_info(topic: str) -> str:
    """Provides info on simple topics."""
    if "python" in topic.lower():
        return "Python is a popular language."
    elif "agent" in topic.lower():
        return "An agent uses an LLM to decide actions."
    return f"No specific info for '{topic}'."

tools = [
    Tool(
        name="info_tool",
        func=get_info,
        description="Useful for getting basic info on a topic.",
    ),
]

# 2. Set up the LLM (requires OPENAI_API_KEY)
# os.environ["OPENAI_API_KEY"] = "YOUR_KEY"
llm = ChatOpenAI(temperature=0, model="gpt-3.5-turbo")

# 3. Get the standard ReAct prompt
prompt = hub.pull("hwchase17/react")

# 4. Create the agent
agent = create_react_agent(llm, tools, prompt)

# 5. Create an agent executor with verbose logging
agent_executor = AgentExecutor(agent=agent, tools=tools, verbose=True)

# 6. Run the agent to see its thought process
agent_executor.invoke({"input": "What is an agent?"})

Decoding Agent Logs

The verbose output shows a clear sequence:

> Entering new AgentExecutor chain...: Agent starts.
Thought:: The LLM's reasoning for the next step.
Action:: The name of the tool chosen.
Action Input:: The arguments passed to the tool.
Observation:: The result returned by the tool.
Final Answer:: The agent's final response after its thoughts.

This structure is your roadmap for debugging!

Wrong Tool for the Job?

One common issue is the agent selecting the wrong tool or providing bad input. Look at the Action: and Action Input: lines.

Did it pick a tool that doesn't fit the query?
Did it extract the wrong information from the query to pass to the tool?

If so, you might need to refine your tool's description or adjust the agent's main prompt to guide it better.

Fixing Agent's Logic

If the agent's Thought: itself seems off, it's a reasoning problem. The LLM might be:

Misunderstanding the overall goal.
Failing to incorporate previous Observations:.
Struggling with complex instructions.

To fix this, clarify the agent's system prompt, provide more context, or break down complex tasks into simpler sub-tasks.

Breaking the Loop

An agent stuck in an infinite loop will repeatedly generate similar Thought:, Action:, and Observation: sequences without progressing to a Final Answer:.

Common causes include:

Ambiguous tool descriptions.
Tools returning unhelpful or identical results.
Prompts that don't clearly define a "completion" state.

Refine tool descriptions, ensure tools provide distinct outputs, or add explicit stopping conditions to your prompt.

Spot the Bug!

An agent is designed to summarize text. Here's a snippet of its verbose trace when asked to summarize "The quick brown fox jumps over the lazy dog":

Thought: I need to summarize the text.
Action: text_summarizer_tool
Action Input: What is the weather like?
Observation: The weather is sunny.
Thought: I need to summarize the text.
Action: text_summarizer_tool
Action Input: What is the weather like?
Observation: The weather is sunny.

What is the primary debugging issue here?

Debugging Agents: Key Takeaways

Congratulations! You've learned how to systematically debug your AI agents. Key points:

Use verbose=True to expose the agent's internal thought process.
Examine Thought, Action, Action Input, and Observation.
Identify issues with tool selection, tool input, or the LLM's reasoning.
Address infinite loops by refining prompts, tool descriptions, or tool outputs.

Happy debugging, and build more robust agents!

자주 묻는 질문

“에이전트 사고 과정 디버깅” 강의는 무료인가요?

네 — “에이전트 사고 과정 디버깅” 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.

“에이전트 사고 과정 디버깅”에서 뭘 배우나요?

에이전트의 추론과 행동 순서에서 문제를 식별하고 해결하기 위한 체계적인 접근법을 적용합니다. 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 2번째 강의입니다.

“에이전트 사고 과정 디버깅” 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.

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