--- title: "How Scale AI does Augmented Data Processing at Massive Scale with Temporal" url: https://daily.dev/posts/how-scale-ai-does-augmented-data-processing-at-massive-scale-with-temporal-qbnlslgno source_url: https://www.youtube.com/watch?v=WrUwy0lA0To type: video:youtube source: "Temporal" published: 2026-05-28T00:14:01.785Z updated: 2026-05-28T00:14:21.455Z tags: ["backend", "ai-agents", "workflow-orchestration"] reading_time: 16 upvotes: 0 comments: 0 language: en --- > ## Documentation Index > Fetch the complete documentation index at: https://daily.dev/llms.txt > Use this file to discover all available pages before exploring further. # How Scale AI does Augmented Data Processing at Massive Scale with Temporal **[Temporal](https://daily.dev/sources/temporal)** · 16 min read · 0 upvotes · 0 comments ## Summary Scale AI engineers describe how they built an augmented data processing platform on top of Temporal for evaluating and improving deployed AI agents. The system uses a generic 'run card' activity pattern where each step reads data, applies a custom transformation, and writes to a dataset service — bypassing Temporal's data size limits by passing URI references between activities instead of raw data. Temporal signals enable an interactive, step-by-step workflow building experience in the UI. The architecture supports joining data across workflows, spawning child workflows for cross-language execution (TypeScript calling a Python service), and eventually running all steps programmatically in one shot. ## Full article daily.dev links to this article rather than hosting it. Read it at the original source: --- Tags: [#backend](https://daily.dev/tags/backend), [#ai-agents](https://daily.dev/tags/ai-agents), [#workflow-orchestration](https://daily.dev/tags/workflow-orchestration) [View this post on daily.dev](https://daily.dev/posts/how-scale-ai-does-augmented-data-processing-at-massive-scale-with-temporal-qbnlslgno)