AI Workflow Orchestration and Queues

AI Workflows Need Orchestration

Production AI applications often run multiple steps: input validation, retrieval, model calls, tool calls, review, writes, notifications, and follow-up actions. Orchestration keeps those steps observable, recoverable, and controlled.

Engineering Patterns

• Use queues for long-running, retryable, or rate-limited tasks.
• Make write operations idempotent where possible.
• Store workflow state, intermediate outputs, and approval status.
• Separate synchronous user experience from background processing.
• Define timeout, retry, dead-letter, and rollback behavior.

Plan for Partial Failure

AI workflows can fail halfway through retrieval, generation, validation, or tool execution. Make failures visible and recoverable instead of hiding them behind a chat response.