Designing an AI agent for low-latency bug report scheduling necessitates a robust, real-time system prioritizing immediate processing and accurate allocation. An event-driven architecture is fundamental to instantly ingest new bug reports, followed by rapid feature engineering extracting critical attributes such as severity, component, and keywords. A lightweight, highly optimized machine learning model, perhaps a gradient boosting machine or a compact neural network, then performs near-instantaneous predictions for optimal assignee and urgency based on historical resolution patterns. The agent must also incorporate developer availability and skill sets as hard constraints, often utilizing a fast heuristic or constraint satisfaction algorithm to find the best fit within milliseconds. To ensure true low latency, the model should be deployed on high-performance infrastructure with optimized inference engines, capable of serving predictions in real time. Furthermore, a continuous feedback loop is vital for the agent to adapt to evolving team dynamics and learn from actual scheduling outcomes, refining its prediction accuracy over time. This integrated approach ensures bug reports are routed to the right individuals with minimal delay, upholding demanding latency targets.