🤖 What are AI Agents?
AI Agents are autonomous systems that can perceive their environment, reason about goals, and take actions to achieve those goals. Unlike simple chatbots, agents can use tools, plan multi-step solutions, and operate independently.
🔍 Perception
Agents understand their environment through sensors, APIs, and data sources
🧠 Reasoning
They plan actions, make decisions, and adapt strategies based on goals
⚡ Action
Agents execute plans through tools, APIs, and environmental changes
Key Insight: Modern AI agents combine LLMs for reasoning with tools for action, creating systems that can solve complex, multi-step problems autonomously.
🏗️ Agent Architectures
Reactive Agents
Simple stimulus-response agents that react to immediate inputs
Characteristics
✅ Pros
- • Fast
- • Predictable
- • Easy to debug
- • Low cost
⚠️ Cons
- • Limited capability
- • No memory
- • Cannot handle complex tasks
Best for: Simple automation, basic Q&A, straightforward tool usage
Implementation Example
from langchain.agents import create_openai_functions_agent, AgentExecutor
from langchain.tools import Tool
from langchain_openai import ChatOpenAI
class ReactiveAgent:
def __init__(self, tools):
self.llm = ChatOpenAI(temperature=0)
self.tools = tools
# Create a simple reactive agent
self.agent = create_openai_functions_agent(
llm=self.llm,
tools=tools,
prompt=self.create_prompt()
)
self.agent_executor = AgentExecutor(
agent=self.agent,
tools=tools,
verbose=True,
max_iterations=3 # Limit iterations for reactive behavior
)
def create_prompt(self):
return """You are a helpful assistant.
Use the available tools to answer questions directly and concisely.
Do not plan ahead or maintain conversation context."""
def run(self, query: str) -> str:
"""Execute query and return immediate response"""
return self.agent_executor.invoke({"input": query})
# Example usage
tools = [search_tool, calculator_tool, weather_tool]
reactive_agent = ReactiveAgent(tools)
response = reactive_agent.run("What's the weather in San Francisco?")🛠️ Agent Tools
Web Search
Search the internet for current information
Use Case: Research, fact-checking, current events
Implementation
from langchain.tools import DuckDuckGoSearchRun
search_tool = DuckDuckGoSearchRun(
name="web_search",
description="Search the internet for current information"
)🚀 Agent Frameworks
LangChain Agents
Built-in agent framework with extensive tool ecosystem
Pros
- • Rich tool ecosystem
- • Multiple agent types
- • Good documentation
Cons
- • Can be complex
- • Limited customization
Best For
Rapid prototyping, standard use cases
Example Code
from langchain.agents import create_openai_functions_agent, AgentExecutor
from langchain.tools import DuckDuckGoSearchRun, CalculatorTool
# Create tools
tools = [DuckDuckGoSearchRun(), CalculatorTool()]
# Create agent
agent = create_openai_functions_agent(llm, tools, prompt)
agent_executor = AgentExecutor(agent=agent, tools=tools)
# Use agent
result = agent_executor.invoke({"input": "What's 15% of 1000?"})AutoGPT
Autonomous agent framework for long-running, self-directed tasks
Pros
- • Autonomous operation
- • Goal persistence
- • Self-improvement
Cons
- • Can be unpredictable
- • Resource intensive
Best For
Long-term autonomous tasks, research projects
Example Code
from langchain.experimental.autonomous_agents import AutoGPT
from langchain.memory import VectorStoreRetrieverMemory
# Create AutoGPT agent
autogpt = AutoGPT.from_llm_and_tools(
ai_name="ResearchAssistant",
ai_role="AI research assistant",
tools=tools,
llm=llm,
memory=VectorStoreRetrieverMemory.from_texts([])
)
# Set goals and run
autogpt.run(["Research the latest developments in quantum computing"])CrewAI
Framework for collaborative multi-agent systems
Pros
- • Easy multi-agent setup
- • Role-based collaboration
- • Process management
Cons
- • Newer framework
- • Limited advanced features
Best For
Team-based problem solving, collaborative workflows
Example Code
from crewai import Agent, Task, Crew
# Define agents
researcher = Agent(
role='Researcher',
goal='Research and gather information',
tools=[search_tool]
)
analyst = Agent(
role='Analyst',
goal='Analyze data and provide insights',
tools=[calculator_tool]
)
# Define tasks
research_task = Task(
description='Research market trends',
agent=researcher
)
analysis_task = Task(
description='Analyze research findings',
agent=analyst
)
# Create crew
crew = Crew(
agents=[researcher, analyst],
tasks=[research_task, analysis_task]
)
# Execute
result = crew.kickoff()🧪 Agent Execution Simulator
🎯 Key Takeaways
Architecture Selection: Choose agent complexity based on task requirements - reactive for simple tasks, autonomous for complex, long-term goals
Tool Integration: Agents are only as powerful as their tools - invest in robust, reliable tool implementations
Safety Considerations: More autonomous agents require stronger safety measures, monitoring, and human oversight
Framework Choice: LangChain for rapid prototyping, CrewAI for collaboration, custom solutions for specialized needs
Testing Strategy: Start with reactive agents, gradually increase complexity as you validate behavior and safety