The Problem I Was Trying to Solve§

The biggest barrier to deploying AI agents in production is their non-deterministic nature. If a database agent generates a query or output format that misses a key field, the frontend app crashes. Traditional frameworks like LangChain or AutoGen treat schemas as post-generation parsers. We wanted to build a type-safe agent using Pydantic AI that enforces strict input/output verification at compilation time and auto-corrects bad model outputs.

Tools and Setup (auto-link injection fires here)§

Our codebase utilized:

  • Pydantic AI Python package.
  • FastAPI for wrapping our agent endpoints.
  • DeepSeek-Coder-V4 as the LLM core.
# Pydantic AI agent defining structured outputs
from pydantic import BaseModel, Field
from pydantic_ai import Agent

class DatabasePatch(BaseModel):
    query: str = Field(description="Sanitized SQL query")
    affected_rows_estimate: int = Field(description="Estimated row changes")

db_patch_agent = Agent('openai:gpt-4o', result_type=DatabasePatch)

Step-by-Step: What I Actually Did§

1. Defining Strict Schemas: We defined our agent parameters using Pydantic classes with detailed field descriptions, which are fed directly to the model as system constraints. 2. Validation Feedback Loops: If the LLM generates a result that fails schema validation, Pydantic AI intercepts the error, appends it to the chat history, and instructs the model to self-correct in a closed loop. 3. Static Analysis Integration: We added mypy type checking to our agent pipeline, ensuring that developers get immediate syntax highlight warnings if a tool parameter violates model specifications.

Results and Takeaways§

  • Zero Runtime Schema Crashes: Schema errors were intercepted and self-corrected before reaching the frontend.
  • Better Developer Experience: Type checking eliminated manual JSON mapping bugs.
  • Type-Safety Is Mandatory: When writing production tools, never parse outputs manually; let Pydantic handle validation.