Production long-horizon agents do not run in `while True`. Every LLM call becomes an activity with checkpoint, retry, and replay. Temporal's OpenAI Agents SDK integration went GA March 2026. Claude Code Routines (Anthropic) runs scheduled Claude Code invocations without a persistent local process. Sessions pause on human-input, survive deploys, and resume from the latest checkpoint keyed by `thread_id`. Behind the new ergonomics sits an old pattern — workflow orchestration — with one new input: LLM calls as non-deterministic activities that must be deterministically replayed on recovery.
Learn 1 hr Python (stdlib, minimal durable-execution state machine)
Consider an agent that runs for four hours. It calls three tools, prompts the user twice, and makes forty LLM calls. Halfway through, the host it is running on reboots. What happens?
In a naive while True loop: everything is lost. The run restarts from scratch. The three tool calls (with real side effects) execute again. The user is prompted again for things they already approved. Forty LLM calls are re-billed.
With durable execution: the run resumes from the most recent checkpoint. Already-completed activities are not re-executed; their results are replayed from the durable log. The user does not re-approve things they already approved. The LLM calls already made are not re-billed.
This is the same pattern workflow engines have shipped for a decade (Temporal, Cadence, Uber's Cherami). What's new is that LLM calls are now a kind of activity — non-deterministic, expensive, with side effects — and they fit this pattern cleanly.
The running theme of the lesson: long-horizon reliability decays (METR observes a "35-minute degradation" — success rate drops roughly quadratically with horizon). Durable execution enables runs that are longer than the reliability profile supports, which is a new way to fail safely if the design is right and unsafely if the design is wrong.
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