Building Intelligent Agents with LangGraph

In the evolving landscape of AI engineering, stateless chains are often insufficient for complex tasks. Enter LangGraph—a framework that allows us to build stateful, multi-actor applications with LLMs.

I recently built a multi-agent investment analysis system and learned a lot about what works (and what doesn’t) in production agent architectures. Here’s my synthesis.

Why State Matters

Most simple RAG applications are “fire and forget”. User asks question → retrieve context → generate response → done.

But real-world processes involve:

Loops: Retry with different strategies if first attempt fails
Branches: Different paths based on intermediate results
Memory: Carrying context across multiple steps
Human-in-the-loop: Pausing for approval, then continuing

LangGraph models these as graphs with nodes (actions) and edges (transitions).

The Mental Model

Think of LangGraph as a state machine for AI:

              ┌─────────┐
              │  START  │
              └────┬────┘
                   │
                   ▼
              ┌────────────┐
         ┌────│   Agent    │────┐
         │    └────────────┘    │
    needs_tool          ready_to_respond
         │                      │
         ▼                      ▼
    ┌─────────┐           ┌─────────┐
    │  Tool   │           │  END    │
    └────┬────┘           └─────────┘
         │
         └───────────────────┐
                             │
                    back_to_agent

Each node is a function. Each edge is a routing decision.

Key Pattern: The ReAct Loop

The most common pattern is ReAct (Reason + Act):

def create_react_agent(tools: list[Tool], llm: ChatModel):
    """Create a ReAct agent using LangGraph."""
    
    # Define the graph
    graph = StateGraph(AgentState)
    
    # Add nodes
    graph.add_node("agent", call_agent)
    graph.add_node("tools", execute_tools)
    
    # Add edges
    graph.add_edge(START, "agent")
    graph.add_conditional_edges(
        "agent",
        should_continue,  # Function that checks if tools needed
        {
            "continue": "tools",
            "end": END
        }
    )
    graph.add_edge("tools", "agent")  # Loop back
    
    return graph.compile()

The agent reasons about what to do, optionally calls tools, then loops until it has an answer.

What I Learned Building Multi-Agent Systems

1. Information Asymmetry is Powerful

In my investment agent system, I gave the Bull and Bear agents different initial information. This forced them to actually discover counterarguments rather than generating token-level balance.

2. Agents Will Game Your System

Without explicit constraints, agents find shortcuts:

Making unfalsifiable claims (“The market could go up or down”)
Appealing to authority without specifics (“Experts say…”)
Hedging everything into meaninglessness

Solution: Add an Evidence Grader that scores claims independently.

3. Temperature Matters Per Agent

Different roles benefit from different creativity levels:

Agent Type	Temperature	Why
Analyst (Bull/Bear)	0.7	Confident, diverse arguments
Synthesizer	0.3	Careful, balanced
Grader	0.1	Consistent, reliable

4. Limit Your Rounds

I tested 1, 2, 3, and 5 debate rounds:

1 round: No real synthesis
2 rounds: Sweet spot—each side responds once
3+ rounds: Agents start agreeing too much, lose diversity

Common Mistakes to Avoid

Too much state: Start with minimal state, add only what you need
Unclear termination: Always define explicit end conditions
No observability: Log every node transition—debugging graph flows is hard otherwise
Ignoring token budgets: Multi-agent = multiple LLM calls = costs add up fast

When NOT to Use LangGraph

Simple, linear workflows (just use LangChain chains)
When you need <1s latency (graph overhead adds ~100-200ms)
Exploratory prototyping (start simpler, add LangGraph later)

Resources

If you’re getting started:

The agent paradigm is young but promising. I’m excited to see what we build.

Have questions or want to discuss agent architectures? Reach out on LinkedIn.