AI Runtime Infrastructure
This addresses the need for more efficient and reliable AI agent operations in real-time applications, representing a novel approach rather than an incremental improvement.
The paper tackles the problem of optimizing AI agent performance during execution by introducing a runtime infrastructure layer that actively monitors and intervenes to improve task success, latency, token efficiency, reliability, and safety, with results including adaptive memory management and failure recovery.
We introduce AI Runtime Infrastructure, a distinct execution-time layer that operates above the model and below the application, actively observing, reasoning over, and intervening in agent behavior to optimize task success, latency, token efficiency, reliability, and safety while the agent is running. Unlike model-level optimizations or passive logging systems, runtime infrastructure treats execution itself as an optimization surface, enabling adaptive memory management, failure detection, recovery, and policy enforcement over long-horizon agent workflows.