A real Tetris loop has time (ticks), concurrent inputs (keystrokes), state transitions (collision, locking, line clears), and non-determinism (piece generation). In many imperative designs, these concerns end up tangled in shared mutable state, which tends to produce bugs that are: hard to reproduce (timing-dependent), hard to test (logic mixed with effects), hard to debug (replay isn't deterministic).
Karrot, a leading platform for building local communities in Korea, uses a recommendation system to provide users with personalized content on the home screen. The system consists of the recommendation machine learning model and a feature platform that acts as a data store for users' behaviour history and article information. As the company has been evolving the recommendation system over recent years, it became apparent that adding new functionality was becoming challenging, and the system began to suffer from limited scalability and poor data quality
Traditional pipelines fall short when building agents that must respond to dynamic, evolving contexts. Gravity tackles this by embracing event-driven architecture and modular orchestration. Agents are modeled as independent services that react to discrete events, allowing the system to flexibly coordinate multiple actors across different stages of a task. Technologies like Temporal, pub/sub messaging, or custom orchestrators can be used to handle event sequencing and retries. The key is decoupling logic.