Orchestrating Multi-Agent Systems with LangGraph

The Problem with Early Agents

Early autonomous agents (like AutoGPT) were notoriously fragile. They would loop infinitely, hallucinate tool calls, and rack up massive API bills.

Deterministic State Machines with LangGraph

To build agents for the enterprise, we need control. We use LangGraph to model agent workflows as explicit state machines (graphs).

Instead of telling an LLM "figure out how to research this company," we define explicit nodes:

1. Planner: Generates a research plan.
2. Web Searcher: Executes queries.
3. Synthesizer: Formats the output.
4. Reviewer: Checks for completeness.

The Graph Structure

from langgraph.graph import StateGraph, END

workflow = StateGraph(AgentState)

# Add nodes
workflow.add_node("planner", plan_research)
workflow.add_node("researcher", execute_search)
workflow.add_node("reviewer", review_findings)

# Define explicit edges to prevent infinite loops
workflow.add_edge("planner", "researcher")
workflow.add_conditional_edges(
    "reviewer",
    check_quality,
    {
        "pass": END,
        "fail": "researcher" # Loop back if quality is low
    }
)

By treating the agent as a state machine, we can implement Human-in-the-Loop approvals and strict exit conditions, making AI agents safe for production.