Designing Multi-Agent Systems: Supervisor-Worker Patterns & LangGraph State Channels
Architecting resilient swarms of specialized agents with deterministic state machines and recursion boundaries.
Executive Summary
Single agents collapse when tasks exceed 5 distinct tool domains. Multi-agent systems decompose complex workflows into specialized nodes coordinated by deterministic supervisor state machines.
Why Single Agents Fail at Scale
When an agent is loaded with dozens of tool schemas, LLM tool selection accuracy drops significantly. Context windows fill with irrelevant tool descriptions, increasing cost and latency.
By decomposing responsibilities into dedicated Research, Analysis, and Output agents, each specialist operates with a focused context and narrow tool suite.
- Tool selection degradation occurs when single agents manage >10 tools
- Specialized agents preserve clean context boundaries
- Supervisor routers guarantee deterministic handoffs
LangGraph State Channels
LangGraph implements graph-based agent state machines where nodes emit delta updates merged into a validated state schema.
This ensures concurrent agents can write to shared state channels without race conditions.
from typing import TypedDict, Sequence
from langgraph.graph import StateGraph, END
class SwarmState(TypedDict):
task: str
research_output: str
code_output: str
verified: bool
def supervisor_node(state: SwarmState) -> dict:
if not state.get("research_output"):
return {"next_agent": "researcher"}
return {"next_agent": "coder"}