lagents

Lagents, short for latency-aware agents, are autonomous software components designed to operate within distributed systems to anticipate, mitigate, or compensate for network or processing delays. They aim to improve responsiveness and user experience by managing resources, routing data, and adjusting quality of service in real time. The concept is used in both research and industry to address the challenges of variable latency across cloud, edge, and end-user networks.

The term lagent is used to describe agents that explicitly incorporate latency as a core factor in decision making. They may be described as latency-aware or latency-optimizing agents. In practice, lagents combine elements from predictive analytics, control theory, and reinforcement learning to forecast delays and select actions that minimize their impact on performance. They often operate within multi-agent ecosystems, coordinating with other agents to balance load, prioritize traffic, and adapt to changing conditions.

Typical capabilities include sensing current network and processing conditions, forecasting near-term latency using time-series models, and executing actions such as dynamic routing, adaptive batching, cache management, or edge offloading. Their decision engines usually employ policies or learned models to optimize objectives like throughput, jitter, or end-to-end response time, while maintaining stability and fairness.

Common applications span distributed cloud services, content delivery networks, online gaming, video conferencing, and Internet of Things ecosystems. Challenges include accurate latency prediction in non-stationary environments, overhead from monitoring, security and privacy concerns, and ensuring interoperability across heterogeneous systems. Research often focuses on scalable architectures and robust learning methods for real-time latency control.