Evaluating LLM Agents in Production: From Prompts to Schema Guardrails

LLM agents are incredibly flexible but struggle with deterministic data constraints. Returning raw Markdown text or malformed JSON can easily crash downstream APIs and databases.

Schema Verification with Pydantic and Zod

We treat model invocations identically to API endpoints. Model outputs must pass verification against structured schemas before committing to local databases. We use validation libraries to request auto-corrections from the model if structures fail.

Example Schema Code

from pydantic import BaseModel, Field

class OrderDetails(BaseModel):
    order_id: str = Field(description="Formatted alphanumeric order reference ID")
    contract_number: int = Field(description="Numerical ID mapped in ERP tables")
    category: str = Field(description="Ticket classification: SHIPPING, BILLING, HELP")