The outcome

Turn a plausible draft into work you are willing to stand behind.

Pydantic AI is a Python agent framework for typed dependencies, validated tools and outputs, model portability, evaluation, observability, and durable execution. Building Python agents with strongly typed inputs, dependencies, tools, and outputs, then evaluating and running them with explicit token, request, tool, concurrency, and durability controls. This guide narrows that broad capability into one repeatable outcome, with checkpoints that keep the source material and your judgment in the loop.

Before you begin

Set the boundary before the tool starts.

Choose one real task, identify who will use the result, and decide what evidence or test will make the result acceptable. Gather only the source material needed for that task. If the work contains confidential, personal, regulated, or client-owned information, confirm that the platform and account are approved before sharing it.

Troiana principle

AI should make the work easier to inspect. If the workflow removes the source, the owner, or the review step, redesign the workflow.

Step by step

A workflow you can repeat.

  1. 01

    Restate the original outcome and acceptance criteria before reviewing the response.

  2. 02

    Mark every factual claim, calculation, quotation, code path, or image detail that could materially affect the result.

  3. 03

    Check those items against the original source, a primary reference, a test, or direct inspection.

  4. 04

    Ask the tool to list assumptions and uncertainty, but do not rely on self-review as the only check.

  5. 05

    Complete a human edit for relevance, tone, privacy, accessibility, and the exact delivery format.

Working standard

What good use looks like.

  • Verify consequential claims independently.
  • Test outputs in the environment where they will be used.
  • Keep a record of sources and review decisions.

Pydantic validation confirms declared types and constraints, not factual correctness, permission, provenance, or harmless side effects. Model-generated tool calls remain untrusted. Enforce current user and tenant authorization inside tools, cap requests, tokens and calls, set timeouts and concurrency, use idempotency for writes, redact telemetry, test replay semantics, and account for integration-specific durability and retry limitations.

Official references

Check the current product documentation.

Features, plan limits, availability, and data controls change. These official pages are the starting points used for this collection.