How Dust Builds Agentic AI with Temporal Workflows

What this post added

This post details how Dust leverages Temporal Workflows as the core orchestration engine for its AI agent platform. It explains how events from various tools (Slack, Notion, GitHub) trigger Temporal Workflows that handle data ingestion, transformation, AI processing, and indexing. It highlights the use of Temporal for Dust's Tracker product, which monitors documents and triggers agent updates. The post emphasizes that Temporal replaces custom logic for retries, timeouts, and state recovery, allowing Dust to focus on product development. It also mentions the scale of operations (10M+ activities/day on Temporal Cloud) and the use of Datadog for observability. A specific example of filtering files within a Temporal Activity for the GitHub connector is provided, illustrating Temporal's retry and resume capabilities.