Skills on steroids: on-demand capabilities in Pydantic AI

User feedback highlighted the demand for skills, specifically progressive disclosure, in AI models, which led to the development of on-demand capabilities to address inefficiencies associated with loading all capabilities upfront. This approach, implemented in Pydantic AI, involves marking capabilities with `defer_loading=True` and using an ID to defer loading until necessary, reducing unnecessary input token costs. The model starts with a catalog of available capabilities and loads them only when required, optimizing resource use. The Pydantic AI framework facilitates this by allowing capabilities to be structurally gated, ensuring they only trigger when loaded, which reduces context and associated costs. Pydantic Logfire provides monitoring to track which capabilities are actually loaded, offering insights and savings, with new users receiving a $10 starter credit for inference through the Pydantic AI Gateway.

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