Depuración de los procesos de razonamiento de los agentes
Aplique métodos sistemáticos para identificar y resolver problemas en las secuencias de razonamiento y acción de su agente.
Depuración de los procesos de razonamiento de los agentes es una lección gratuita de AI Agents with LangChain & Autonomous Workflows en CoddyKit. Esta es la lección 2 de 4. Puedes leer la lección completa abajo gratuitamente — luego la practicas en el navegador con un editor de código integrado y un tutor de IA 24/7. Forma parte de la ruta de aprendizaje de AI Agents with LangChain & Autonomous Workflows, y tu progreso se sincroniza en la web y la app de CoddyKit. El curso de AI Agents with LangChain & Autonomous Workflows incluye 4 lecciones en total.
Partes de esta lección aún no han sido traducidas y se muestran en inglés.
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=Trueto 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!
Preguntas frecuentes
¿La lección «Depuración de los procesos de razonamiento de los agentes» es gratis?
Sí — el texto completo de «Depuración de los procesos de razonamiento de los agentes» es gratis para leer aquí en la web. Para practicarla de forma interactiva (editor de código integrado y tutor de IA 24/7) y desbloquear el resto del curso de AI Agents with LangChain & Autonomous Workflows, actualiza a CoddyKit PRO. El curso de AI Agents with LangChain & Autonomous Workflows incluye 4 lecciones en total.
¿Qué aprenderé en «Depuración de los procesos de razonamiento de los agentes»?
Aplique métodos sistemáticos para identificar y resolver problemas en las secuencias de razonamiento y acción de su agente. Practicas AI Agents with LangChain & Autonomous Workflows con código real que ejecutas directamente en el navegador, y un tutor de IA 24/7 responde tus preguntas mientras trabajas en la lección.
¿Necesito experiencia previa para empezar AI Agents with LangChain & Autonomous Workflows?
No se requiere experiencia previa. AI Agents with LangChain & Autonomous Workflows en CoddyKit está estructurado para principiantes hasta estudiantes avanzados, así que puedes empezar aquí o desde el inicio y avanzar a tu ritmo. Esta es la lección 2 de 4.
¿Cuánto tiempo toma la lección «Depuración de los procesos de razonamiento de los agentes»?
La mayoría de las lecciones de CoddyKit toman alrededor de 5–10 minutos. Cada una es compacta e interactiva, así que avanzas constantemente y retomas exactamente por donde dejaste en la web y la app.
¿Puedo escribir y ejecutar código en esta lección de AI Agents with LangChain & Autonomous Workflows?
Sí. Cada lección de AI Agents with LangChain & Autonomous Workflows incluye un editor de código integrado, así que escribes y ejecutas código real directamente en tu navegador y obtienes retroalimentación instantánea de IA — sin configuración local necesaria.
Todas las lecciones de este curso
- LangSmith para trazas y supervisión
- Depuración de los procesos de razonamiento de los agentes
- Evaluación del rendimiento de los agentes
- Uso de tokens y monitorización de costes