Retry With Backoff and When to Route to a Human
Understand how to design and apply retry strategies with backoff for transient errors in AI workflows, and decide when it is necessary to escalate support tickets to human reviewers based on failure type and delay budgets. This lesson teaches how to balance automation reliability with prompt human intervention while ensuring proper logging and error handling.
Support tickets keep arriving, and the support triage call sits in the middle of a workflow that cannot stall. If one LLM API call fails and blocks the queue, the backlog grows. If the workflow drops the ticket on the floor, the miss stays invisible until a customer follows up.
A retry is not a free fix. Each attempt adds delay and spends another request, so the workflow needs a deliberate control that trades a small, bounded delay for fewer human escalations, without spamming the API or hiding failures.
What a retry policy must specify
With the workflow pressure clear, the next step is to make a retry policy concrete enough that an automation tool can implement it later. A minimal policy answers three questions. Which failures qualify for another attempt, how many total attempts are allowed, and how long the workflow waits between ...