Tipos de agentes y toma de decisiones
Explore distintos tipos de agentes, como ReAct y los conversacionales, y los mecanismos subyacentes que utilizan para decidir qué herramienta usar.
Tipos de agentes y toma de decisiones 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.
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:
- Thought: The LLM reasons about the current situation and what to do next.
- Action: Based on the thought, the LLM chooses a tool and its input.
- 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!
Preguntas frecuentes
¿La lección «Tipos de agentes y toma de decisiones» es gratis?
Sí — el texto completo de «Tipos de agentes y toma de decisiones» 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 «Tipos de agentes y toma de decisiones»?
Explore distintos tipos de agentes, como ReAct y los conversacionales, y los mecanismos subyacentes que utilizan para decidir qué herramienta usar. 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 «Tipos de agentes y toma de decisiones»?
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
- Definición y uso de herramientas
- Tipos de agentes y toma de decisiones
- Uso de toolkits prediseñados
- Gestión de errores y ejecución segura de herramientas