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AI Runtime Infrastructure

Christopher Cruz

TL;DR

This work introduces 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.

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.

AI Runtime Infrastructure

TL;DR

This work introduces 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.

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.
Paper Structure (26 sections, 1 figure)

This paper contains 26 sections, 1 figure.

Table of Contents

  1. Introduction
  2. Defining AI Runtime Infrastructure
  3. Architectural Positioning
  4. Design Principles for AI Runtime Infrastructure
  5. Execution-Time Intervention
  6. Long-Horizon State Awareness
  7. Closed-Loop Control
  8. Model-Agnostic Operation
  9. Application-Agnostic Control
  10. Safety, Cost, and Reliability as Runtime Concerns
  11. Early Systems and Precursors
  12. VIGIL: A Runtime-Aware Precursor
  13. Adaptive Focus Memory: AI Runtime Infrastructure
  14. From Precursors to Runtime Infrastructure
  15. Related Work
  16. ...and 11 more sections

Figures (1)

  • Figure 1: The full agentic AI systems stack. AI runtime infrastructure operates as an execution-time control layer between agent orchestration and model serving, observing execution state and intervening during runtime to optimize task success, efficiency, reliability, and safety. Observability and evaluation systems span the stack but do not influence execution-time behavior.