Editing AI Agents and Agentic Systems (section)

== <span style="color: #FFFFFF;">Applying</span> ==
'''Building a simple ReAct agent with LangChain:'''

<syntaxhighlight lang="python">
from langchain.agents import create_react_agent, AgentExecutor
from langchain_openai import ChatOpenAI
from langchain_community.tools import DuckDuckGoSearchRun
from langchain_experimental.tools import PythonREPLTool
from langchain import hub

# Available tools
tools = [
    DuckDuckGoSearchRun(),
    PythonREPLTool()  # Execute Python code in a sandbox
]

# Load ReAct prompt template
prompt = hub.pull("hwchase17/react")

# LLM backbone
llm = ChatOpenAI(model="gpt-4o", temperature=0)

# Create agent
agent = create_react_agent(llm, tools, prompt)

# Executor handles the loop
executor = AgentExecutor(
    agent=agent,
    tools=tools,
    verbose=True,         # Print the reasoning trace
    max_iterations=10,    # Prevent infinite loops
    handle_parsing_errors=True
)

result = executor.invoke({
    "input": "Find the top 3 trending AI papers from this week and summarize their key findings."
})
print(result["output"])
</syntaxhighlight>

; Multi-agent patterns
: '''Sequential''' → Agent A produces output → Agent B processes it → Agent C finalizes. Simple, predictable.
: '''Parallel''' → Multiple agents work simultaneously on subtasks; results are aggregated.
: '''Hierarchical''' → Orchestrator agent delegates to worker agents, reviews their outputs, and re-delegates if needed.
: '''Debate''' → Two agents argue opposing positions; a judge agent synthesizes the best answer.
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