AI Runtime Infrastructure
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Abstract (arXiv)
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.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 7 of its 11 references that are in the map. First posted on arXiv 28 Feb 2026.
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7 of 11 references.
References · 7
- 2022ReAct: Synergizing Reasoning and Acting in Language Modelscited by 8,355
- 2023Reflexion: Language Agents with Verbal Reinforcement Learningcited by 4,064
- 2023Generative Agents: Interactive Simulacra of Human Behaviorcited by 3,500
- 2023Self-Refine: Iterative Refinement with Self-Feedbackcited by 3,317
- 2023Teaching Large Language Models to Self-Debugcited by 963
- 2025Adaptive Focus Memory for Language Modelscited by 6
- 2025VIGIL: A Reflective Runtime for Self-Healing Agentscited by 4
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