Why your AI agent needs a task queue (and how to build one)

AI systems often experience failures due to the inherent variability and complexity of their operations, resulting in issues like silent data loss, duplicate processing, and race conditions. To address these challenges, implementing a task queue is crucial as it provides ordering, observability, and the ability to resume or replay operations, thus preserving context and preventing duplication. Task queues help manage the unique challenges of AI workloads, such as variable token consumption and rate limits, by enabling adaptive throttling and ensuring retries are deterministic. They serve as a source of truth by maintaining the complete context needed for each operation, which is especially important when dealing with multi-step tasks that may encounter failures at different stages. Additionally, the dead letter queue becomes a valuable tool for debugging systemic issues and allows for safe retries once the root causes are addressed, ultimately transforming AI agents from fragile scripts into resilient systems.