Principle:PrefectHQ Prefect AI Agent Configuration
| Metadata | |
|---|---|
| Sources | pydantic-ai, pydantic-ai Agents |
| Domains | AI_Agents, LLM |
| Last Updated | 2026-02-09 00:00 GMT |
Overview
A pattern for configuring AI agents with typed tools, structured output schemas, and system prompts to create reliable, task-specific AI workflows.
Description
AI Agent Configuration defines how to set up an LLM-powered agent with: a specific model (e.g., openai:gpt-4o), typed tool functions the agent can call, a Pydantic output schema for structured responses, dependency injection for runtime context (e.g., a DataFrame), and a system prompt that guides agent behavior. This pattern separates agent configuration from execution, making agents reusable and testable.
Usage
Use this pattern when building AI-powered workflows that need structured outputs, tool use, and type-safe dependency injection. It is the foundation for creating agents that can autonomously analyze data, make decisions, or interact with external systems.
Theoretical Basis
The Agent pattern from AI engineering: an LLM is configured with tools (functions it can call), constraints (output schema), and context (system prompt + dependencies). The agent autonomously decides which tools to call and how to structure its response. Key design decisions:
- typed deps provide runtime context without embedding data in prompts
- output_type enforces response structure via Pydantic validation
- tool functions define the agent's capabilities