How to Use PydanticAI for Agents workflows

Pydantic AI is the Python AI SDK from the Pydantic team for building type-safe agents and LLM applications. Confirm live details on pydantic.dev/docs/ai before production use.

Practical Agents workflows examples

Example 1

Scenario:
PydanticAI — Agents workflows (pass 1). Context: Pydantic AI is the Python AI SDK from the Pydantic team for building type-safe agents and LLM applications.

Objective:
Deliver a reviewable agents workflows result using PydanticAI.

Inputs:
- Verified facts from pydantic.dev/docs/ai
- Audience, channel, or technical constraints
- Success criteria and forbidden claims
- Relevant product surfaces: Agents, API, Open Source

Workflow:
Open PydanticAI → Configure for agents workflows → Pilot with sample inputs → Review against success criteria → Iterate one axis → Finalize

Requirements:
- Use only verified PydanticAI capabilities; do not invent features.
- Confirm live details on pydantic.dev/docs/ai before promising volume or pricing.
- Human-review before external publish, send, billing, or compliance use.

Expected output:
A concrete agents workflows artifact plus a short verification checklist.

Example 2

Scenario:
PydanticAI — Agents workflows (pass 2). Context: Pydantic AI is the Python AI SDK from the Pydantic team for building type-safe agents and LLM applications.

Objective:
Deliver a reviewable agents workflows result using PydanticAI.

Inputs:
- Verified facts from pydantic.dev/docs/ai
- Audience, channel, or technical constraints
- Success criteria and forbidden claims
- Relevant product surfaces: Agents, API, Open Source

Workflow:
Open PydanticAI → Configure for agents workflows → Pilot with sample inputs → Review against success criteria → Iterate one axis → Finalize

Requirements:
- Use only verified PydanticAI capabilities; do not invent features.
- Confirm live details on pydantic.dev/docs/ai before promising volume or pricing.
- Human-review before external publish, send, billing, or compliance use.

Expected output:
A concrete agents workflows artifact plus a short verification checklist.

Example 3

Scenario:
PydanticAI — Agents workflows (pass 3). Context: Pydantic AI is the Python AI SDK from the Pydantic team for building type-safe agents and LLM applications.

Objective:
Deliver a reviewable agents workflows result using PydanticAI.

Inputs:
- Verified facts from pydantic.dev/docs/ai
- Audience, channel, or technical constraints
- Success criteria and forbidden claims
- Relevant product surfaces: Agents, API, Open Source

Workflow:
Open PydanticAI → Configure for agents workflows → Pilot with sample inputs → Review against success criteria → Iterate one axis → Finalize

Requirements:
- Use only verified PydanticAI capabilities; do not invent features.
- Confirm live details on pydantic.dev/docs/ai before promising volume or pricing.
- Human-review before external publish, send, billing, or compliance use.

Expected output:
A concrete agents workflows artifact plus a short verification checklist.

Example 4

Scenario:
PydanticAI — Agents workflows (pass 4). Context: Pydantic AI is the Python AI SDK from the Pydantic team for building type-safe agents and LLM applications.

Objective:
Deliver a reviewable agents workflows result using PydanticAI.

Inputs:
- Verified facts from pydantic.dev/docs/ai
- Audience, channel, or technical constraints
- Success criteria and forbidden claims
- Relevant product surfaces: Agents, API, Open Source

Workflow:
Open PydanticAI → Configure for agents workflows → Pilot with sample inputs → Review against success criteria → Iterate one axis → Finalize

Requirements:
- Use only verified PydanticAI capabilities; do not invent features.
- Confirm live details on pydantic.dev/docs/ai before promising volume or pricing.
- Human-review before external publish, send, billing, or compliance use.

Expected output:
A concrete agents workflows artifact plus a short verification checklist.

Example 5

Scenario:
PydanticAI — Agents workflows (pass 5). Context: Pydantic AI is the Python AI SDK from the Pydantic team for building type-safe agents and LLM applications.

Objective:
Deliver a reviewable agents workflows result using PydanticAI.

Inputs:
- Verified facts from pydantic.dev/docs/ai
- Audience, channel, or technical constraints
- Success criteria and forbidden claims
- Relevant product surfaces: Agents, API, Open Source

Workflow:
Open PydanticAI → Configure for agents workflows → Pilot with sample inputs → Review against success criteria → Iterate one axis → Finalize

Requirements:
- Use only verified PydanticAI capabilities; do not invent features.
- Confirm live details on pydantic.dev/docs/ai before promising volume or pricing.
- Human-review before external publish, send, billing, or compliance use.

Expected output:
A concrete agents workflows artifact plus a short verification checklist.

Checklist before you ship

• Confirm the workflow stays inside verified PydanticAI capabilities
• Review outputs against pydantic.dev/docs/ai when accuracy or pricing claims matter
• Keep a short verification list for any claim you would publish externally