AI Runtime Infrastructure

TL;DR

This paper presents AI Runtime Infrastructure, a novel execution layer that actively manages and optimizes agent behavior during runtime to improve performance, safety, and reliability.

Contribution

It introduces a new runtime layer that operates above models to optimize agent execution through active observation, reasoning, and intervention.

Findings

Enables adaptive memory management during agent execution

Improves task success and safety through active intervention

Optimizes latency and token efficiency in real-time

Abstract

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.