Principle:PrefectHQ Prefect Durable AI Execution
| Metadata | |
|---|---|
| Sources | pydantic-ai Prefect |
| Domains | AI_Agents, Fault_Tolerance |
| Last Updated | 2026-02-09 00:00 GMT |
Overview
A pattern that wraps AI agent execution with Prefect's durable execution semantics to provide automatic retries, timeouts, and idempotency for LLM and tool calls.
Description
Durable AI Execution solves the reliability problem of AI workflows. LLM API calls are expensive, slow, and prone to transient failures (rate limits, timeouts, server errors). By wrapping a pydantic-ai agent with PrefectAgent, each LLM call and each tool call becomes an independent Prefect task with its own retry policy and timeout. This means:
- Failed LLM calls are retried without re-running successful tool calls
- Tool calls have separate retry/timeout configs
- The entire agent execution is observable in the Prefect UI
- Deployed flows gain transactional semantics for idempotent reruns
Usage
Use this pattern whenever running AI agents in production where reliability, observability, and cost efficiency matter. It is especially important when agents make multiple LLM calls or use tools that can fail independently.
Theoretical Basis
Durable execution wraps non-deterministic operations (LLM calls) with deterministic replay semantics. Each operation is checkpointed as a Prefect task. On retry, completed tasks are skipped (idempotency) and only failed operations are re-executed. This prevents duplicate API calls and wasted compute. Configuration via TaskConfig allows different retry/timeout policies for LLM calls (expensive, slow) vs tool calls (cheap, fast).