<\/figure>\n\n\n\nLet’s build a practical agent that can research topics and write reports. This example demonstrates all five core components in action.<\/p>\n\n\n\n
1. Set Up the Environment<\/h3>\n\n\n\nInstall required packages<\/h4>\n\n\n\n pip install langchain langchain-openai tavily-python<\/p>\n\n\n\n
Set up environment variables<\/h4>\n\n\n\n export OPENAI_API_KEY=”your-key-here”<\/p>\n\n\n\n
export TAVILY_API_KEY=”your-key-here”<\/p>\n\n\n\n
2. Initialize the Core Components<\/h3>\n\n\n\n from langchain_openai import ChatOpenAI<\/p>\n\n\n\n
from langchain.agents import create_react_agent, AgentExecutor<\/p>\n\n\n\n
from langchain.memory import ConversationBufferMemory<\/p>\n\n\n\n
from langchain_community.tools.tavily_search import TavilySearchResults<\/p>\n\n\n\n
from langchain.tools import Tool<\/p>\n\n\n\n
from langchain import hub<\/p>\n\n\n\n
Initialize the LLM<\/h4>\n\n\n\n llm = ChatOpenAI(model=”gpt-4.1-mini”, temperature=0)<\/p>\n\n\n\n
Set up memory<\/h4>\n\n\n\n memory = ConversationBufferMemory(<\/p>\n\n\n\n
memory_key=”chat_history”,<\/p>\n\n\n\n
return_messages=True<\/p>\n\n\n\n
)<\/p>\n\n\n\n
Configure tools<\/h4>\n\n\n\n search = TavilySearchResults(max_results=5)<\/p>\n\n\n\n
tools = [<\/p>\n\n\n\n
Tool(<\/p>\n\n\n\n
name=”Search”,<\/p>\n\n\n\n
func=search.run,<\/p>\n\n\n\n
description=”Search for current information on any topic. Returns relevant results.”<\/p>\n\n\n\n
)<\/p>\n\n\n\n
]<\/p>\n\n\n\n
Load ReAct prompt (handles planning)<\/h4>\n\n\n\n prompt = hub.pull(“hwchase17\/react”)<\/p>\n\n\n\n
3. Create the Agent<\/h3>\n\n\n\nCreate the ReAct agent<\/h4>\n\n\n\n agent = create_react_agent(<\/p>\n\n\n\n
llm=llm,<\/p>\n\n\n\n
tools=tools,<\/p>\n\n\n\n
prompt=prompt<\/p>\n\n\n\n
)<\/p>\n\n\n\n
Set up the execution loop<\/h4>\n\n\n\n agent_executor = AgentExecutor(<\/p>\n\n\n\n
agent=agent,<\/p>\n\n\n\n
tools=tools,<\/p>\n\n\n\n
memory=memory,<\/p>\n\n\n\n
verbose=True, # See the agent’s thinking process<\/p>\n\n\n\n
handle_parsing_errors=True,<\/p>\n\n\n\n
max_iterations=10 # Prevent infinite loops<\/p>\n\n\n\n
)<\/p>\n\n\n\n
4. Run Your Agent<\/h3>\n\n\n\nExample: Research and report on AI agents<\/h4>\n\n\n\n result = agent_executor.invoke({<\/p>\n\n\n\n
“input”: “Research the latest developments in AI agents for July 2025 and write a brief report highlighting the top 3 trends.”<\/p>\n\n\n\n
})<\/p>\n\n\n\n
print(result[“output”])<\/p>\n\n\n\n
What Makes This Work<\/h4>\n\n\n\n The agent will autonomously search multiple times, synthesize information, and produce a coherent report. It’s not following a script\u2014it’s dynamically deciding what to search for based on what it finds.<\/p>\n\n\n\n
Advanced Techniques That Improve Performance<\/h3>\n\n\n\n After analyzing thousands of agent interactions, here are the techniques that genuinely improve agent performance:<\/p>\n\n\n\n
1. Decomposition Over Role-Playing<\/h4>\n\n\n\n\nWhat doesn’t work:<\/strong> Role prompting (e.g., “You are an expert researcher…”) has little to no effect on accuracy.<\/li>\n\n\n\nWhat does work:<\/strong> Asking agents to decompose problems into sub-tasks:<\/li>\n<\/ul>\n\n\n\ndecomposition_prompt = “””<\/p>\n\n\n\n
Break this task into smaller steps:<\/p>\n\n\n\n
\nFirst, identify the key components<\/li>\n\n\n\n Then, tackle each component separately<\/li>\n\n\n\n Finally, synthesize the results<\/li>\n<\/ul>\n\n\n\nTask: {task} <\/p>\n\n\n\n
“””<\/p>\n\n\n\n
2. Self-Criticism and Reflection<\/h4>\n\n\n\n Adding a self-criticism step dramatically improves output quality:<\/p>\n\n\n\n
reflection_prompt = “””<\/p>\n\n\n\n
Review your previous response and identify:<\/p>\n\n\n\n
\nAny logical errors or inconsistencies<\/li>\n\n\n\n Missing important information<\/li>\n\n\n\n Areas that could be clearer<\/li>\n<\/ul>\n\n\n\nPrevious response: {response}<\/p>\n\n\n\n
“””<\/p>\n\n\n\n
3. Context Over Instructions<\/h4>\n\n\n\n Context is massively underrated. Simply providing more relevant background information improves performance more than complex prompting techniques:<\/p>\n\n\n\n
Less effective:<\/strong><\/p>\n\n\n\nprompt = “Write a report about AI agents”<\/p>\n\n\n\n
More effective:<\/strong><\/p>\n\n\n\nprompt = “””Write a report about AI agents.<\/p>\n\n\n\n
Context: AI agents are autonomous systems that can plan and execute tasks. <\/p>\n\n\n\n
They differ from chatbots by making dynamic decisions rather than following scripts.<\/p>\n\n\n\n
Key frameworks include LangChain, AutoGen, and CrewAI.<\/p>\n\n\n\n
The report is for technical readers familiar with AI concepts.<\/p>\n\n\n\n
“””<\/p>\n\n\n\n
Common Mistakes That Break AI Agents<\/h3>\n\n\n\n Here are the top mistakes I see repeatedly:<\/p>\n\n\n\n
1. Infinite Loops Without Limits<\/h4>\n\n\n\n Always set max_iterations: Agents can get stuck in loops. Set reasonable limits and implement timeout handling.<\/p>\n\n\n\n
2. Poor Tool Descriptions<\/h4>\n\n\n\n Bad:<\/strong><\/p>\n\n\n\nTool(name=”search”, description=”searches for stuff”)<\/p>\n\n\n\n
Good:<\/strong><\/p>\n\n\n\nTool(<\/p>\n\n\n\n
name=”WebSearch”,<\/p>\n\n\n\n
description=”Search the web for current information. Use for: recent news, current events, factual data, company information. Returns 5 most relevant results.”<\/p>\n\n\n\n
)<\/p>\n\n\n\n
3. Ignoring Error States<\/h4>\n\n\n\n Agents will encounter errors. Plan for them:<\/p>\n\n\n\n
Try:<\/strong><\/p>\n\n\n\n result = agent_executor.invoke({“input”: user_query})<\/p>\n\n\n\n
except Exception as e: <\/p>\n\n\n\n
Don’t just fail – help the agent recover<\/h4>\n\n\n\n recovery_prompt = f”The previous action failed with error: {e}. Try an alternative approach.”<\/p>\n\n\n\n
result = agent_executor.invoke({“input”: recovery_prompt})<\/p>\n\n\n\n
4. Overlooking Token Costs<\/h4>\n\n\n\n Agents can consume tokens rapidly. Monitor and optimize:<\/p>\n\n\n\n
\nUse smaller models when possible (GPT-4.1-mini vs GPT-4.1)<\/li>\n\n\n\n Implement summary memory for long conversations<\/li>\n\n\n\n Cache tool results to avoid repeated calls<\/li>\n<\/ul>\n\n\n\nProduction-Ready Agent Architectures<\/h3>\n\n\n\n For production systems, choose your architecture based on your needs:<\/p>\n\n\n\nFramework<\/strong><\/td>Best For<\/strong><\/td>Architecture<\/strong><\/td>Key Strength<\/strong><\/td><\/tr>LangChain + LangGraph<\/td> Complex single agents<\/td> Graph-based execution<\/td> Modular, extensive tools<\/td><\/tr> AutoGen<\/td> Multi-agent systems <\/td> Event-driven actors<\/td> Agent collaboration<\/td><\/tr> CrewAI<\/td> Team-based workflows<\/td> Role-based agents<\/td> Natural team dynamics<\/td><\/tr> Custom<\/td> Specific requirements <\/td> Your choice<\/td> Natural team dynamics<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\nLangChain + LangGraph Architecture<\/h3>\n\n\n\n LangChain has evolved into the de facto standard for single-agent systems. The addition of LangGraph in 2025 brings sophisticated state management:<\/p>\n\n\n\n
from langgraph.graph import StateGraph, State<\/p>\n\n\n\n
from typing import TypedDict<\/p>\n\n\n\n
class AgentState(TypedDict):<\/p>\n\n\n\n
messages: list<\/p>\n\n\n\n
current_task: str<\/p>\n\n\n\n
completed_tasks: list<\/p>\n\n\n\n
Define the Graph<\/h4>\n\n\n\n workflow = StateGraph(AgentState)<\/p>\n\n\n\n
Add Nodes for Different Agent Capabilities<\/h4>\n\n\n\n workflow.add_node(“researcher”, research_node)<\/p>\n\n\n\n
workflow.add_node(“writer”, writing_node)<\/p>\n\n\n\n
workflow.add_node(“reviewer”, review_node)<\/p>\n\n\n\n
Define the Flow<\/h4>\n\n\n\n workflow.add_edge(“researcher”, “writer”)<\/p>\n\n\n\n
workflow.add_edge(“writer”, “reviewer”)<\/p>\n\n\n\n
AutoGen Multi-Agent Architecture<\/h3>\n\n\n\n Microsoft’s AutoGen excels when you need multiple specialized agents working together:<\/p>\n\n\n\n
import autogen<\/p>\n\n\n\n
Define Specialized Agents<\/h4>\n\n\n\n researcher = autogen.AssistantAgent(<\/p>\n\n\n\n
name=”Researcher”,<\/p>\n\n\n\n
system_message=”You are a research specialist. Find and verify information.”<\/p>\n\n\n\n
)<\/p>\n\n\n\n
writer = autogen.AssistantAgent(<\/p>\n\n\n\n
name=”Writer”,<\/p>\n\n\n\n
system_message=”You are a technical writer. Create clear, accurate content.”<\/p>\n\n\n\n
)<\/p>\n\n\n\n
critic = autogen.AssistantAgent(<\/p>\n\n\n\n
name=”Critic”,<\/p>\n\n\n\n
system_message=”You review work for accuracy and clarity. Be constructive but thorough.”<\/p>\n\n\n\n
)<\/p>\n\n\n\n
Create a Group Chat for Collaboration<\/h4>\n\n\n\n groupchat = autogen.GroupChat(<\/p>\n\n\n\n
agents=[researcher, writer, critic],<\/p>\n\n\n\n
messages=[],<\/p>\n\n\n\n
max_round=10<\/p>\n\n\n\n
)<\/p>\n\n\n\n
Real-World Examples of Working AI Agents<\/h3>\n\n\n\n Let’s look at actual agents being used in production today (see more real working AI agent examples):<\/p>\n\n\n\n
1. Customer Service Agent (E-commerce)<\/h4>\n\n\n\n This agent handles complete customer interactions autonomously (learn more in our customer service automation guide):<\/p>\n\n\n\n
\nChecks order status in the database<\/li>\n\n\n\n Processes returns and refunds<\/li>\n\n\n\n Updates shipping addresses<\/li>\n\n\n\n Escalates complex issues to humans<\/li>\n<\/ul>\n\n\n\nKey Innovation: <\/strong>Uses multiple specialized tools (database queries, payment processing, shipping APIs) selected dynamically based on customer needs.<\/p>\n\n\n\n2. Code Review Agent (Software Development)<\/h4>\n\n\n\n Automatically reviews pull requests:<\/p>\n\n\n\n