Type-Safe Agentic Workflows with PydanticAI

The Grind

Writing brittle glue code to parse LLM outputs and manually managing tool-calling states makes production agents difficult to scale.

The AI Workflow

from pydantic_ai import Agent
from pydantic import BaseModel

class QueryResult(BaseModel):
    sql: str
    risk_score: int

# Define a production-grade agent with built-in validation
analyst = Agent('openai:gpt-4o', result_type=QueryResult)

@analyst.tool
def get_schema(table_name: str) -> str:
    return f'Schema for {table_name}: id (int), val (float)'

# The framework handles the loop, tool calls, and validation
result = analyst.run_sync('Analyze the risk of the users table')
print(result.data.sql)

PRO

The Lever

Eliminate 60% of agent boilerplate by replacing manual JSON parsing with native Pydantic validation for 10x more reliable structured outputs.