1. ReAct & Multi-Agent Paradigms
Single-prompt LLM execution breaks down when faced with complex multi-step enterprise workflows. The ReAct (Reasoning + Acting) loop combined with multi-agent specialization allows large tasks to be decomposed into isolated, verifiable sub-actions executed by purpose-built agents. Discover AIConnect’s Enterprise Multi-Agent Orchestration Engine.
2. LangGraph Multi-Agent Code Implementation
The code below illustrates building a multi-agent graph with supervisor routing and tool execution in Python:
// multi_agent_graph.py - LangGraph Supervisor Pattern
from typing import TypedDict, Annotated, List
from langgraph.graph import StateGraph, END
class AgentState(TypedDict):
messages: List[str]
next_step: str
task_completed: bool
def supervisor_agent(state: AgentState):
print("Supervisor analyzing task breakdown...")
# Routing logic
return {"next_step": "code_executor", "task_completed": False}
def code_executor(state: AgentState):
print("Code Executor running Python sandbox test...")
return {"messages": ["✓ Tests passed."], "next_step": END, "task_completed": True}
workflow = StateGraph(AgentState)
workflow.add_node("supervisor", supervisor_agent)
workflow.add_node("code_executor", code_executor)
workflow.set_entry_point("supervisor")
workflow.add_edge("supervisor", "code_executor")
graph = workflow.compile()
print("✓ Enterprise Multi-Agent Graph Compiled Successfully.")Indexed Topics & Tech Keywords
#Custom AI Agents#Multi-Agent Systems#Agent Orchestration#Enterprise AI#LangGraph#ReAct Pattern