11 Design Patterns for Building Polling Agents in AI Assistants

Polling agents are background processes in AI assistant architecture that repeatedly monitor a data source and trigger actions when specific conditions are met. Unlike standard chat assistants that respond only to user queries, polling agents proactively watch inboxes, task lists, GitHub issues, or job queues on the user's behalf. A well-structured polling agent handles five core responsibilities: waking on schedule, reading the source, tracking previously seen state, evaluating whether new data matters, and acting exactly once without duplication. Developers are advised to keep the language model focused on semantic interpretation and language generation, while delegating scheduling, state management, retries, and locking to standard backend infrastructure. Durable state records — storing metadata such as poll ID, source reference, condition, run timestamps, and failure count — are essential for reliable production deployments.