Pydantic AI: agent building features and setup

Overview

Pydantic AI suits cases where a model's response needs to become data in software instead of remaining text in a conversation. Type definitions help check that output matches the structure the system expects. The same foundation can be used for an agent behind an interface, an internal tool, or a background task. Keep in mind that validating structure does not verify factual accuracy, so add business checks for important fields.

Features

• A Python agent loop with types and data structure validation.
• The option to choose different model providers within the same development environment.
• Integrating tools and agent capabilities into different workflows and interfaces.

Business uses

• Extracting fields from a request into a data form the system can validate.
• An internal agent that returns a structured recommendation with rationale and sources for review.

Getting started

• Define a data model for the output with required fields and clear types.
• Connect a model provider and test valid, incomplete, and contradictory examples.
• Add business checks before writing the output to a database or triggering an action.

What to check

• Output that passes type validation can still contain an incorrect amount or date.
• Check capability compatibility across model providers, especially when relying on tools or structured output.