Validation Nodes: The Quality Gatekeeper

In Module 3, we discussed the Critic and Validator pattern. Now, we learn how to implement it technically within a LangGraph. A Validation Node is a checkpoint that occurs after the "Working" node but before the "End" node.

1. The "Zero Trust" Architecture

In high-stakes software, we assume the LLM might hallucinate.

• The Worker Node: Writes the code.
• The Validation Node: Runs mypy or pytest on the code.
• The Edge: If tests fail, it sends the error log back to the Worker Node for fixing.

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2. Visualizing the Validation Cycle

graph LR
    Draft[Node: Generate Python Script] --> Validate[Node: Run Unit Tests]
    Validate --> Router{Test Pass?}
    Router -- No --> Draft
    Router -- Yes --> END

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3. Types of Validation

A. Format Validation

Does the output match our required JSON schema?

def validate_json(state: State):
    content = state["messages"][-1].content
    try:
        json.loads(content)
        return {"is_valid": True}
    except:
        return {"messages": ["Error: Please return only valid JSON."], "is_valid": False}

B. Logical Validation (The Critic)

Is the answer factually consistent with our documents?

• This uses a separate "Judge" LLM prompt.

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4. Avoiding the "Infinite Fixing" Loop

If the Validator keeps rejecting the output, the agent might get stuck.

• Solution: After 3 failed validations, the graph should route to a Human-in-the-Loop node or return a "Partial success" error instead of continuing the loop.

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5. Code Example: Defining the Conditional Path

workflow.add_node("generator", generate_code)
workflow.add_node("validator", run_unit_tests)

workflow.add_edge("generator", "validator")

workflow.add_conditional_edges(
    "validator",
    lambda state: "fixing" if not state["is_valid"] else "success",
    {
        "fixing": "generator",
        "success": END
    }
)

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Key Takeaways

• Validation Nodes significantly increase the reliability of agentic pipelines.
• Structured checks (JSON, Pydantic, Code tests) are superior to simple LLM "guessing."
• Validation errors should be fed back to the generator as Helpful Context.
• Always guard against Validation Loops using state counters.