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Agency proposed as new organizing abstraction for computer science

By PulseAugur Editorial · [2 sources] ·

A new paper proposes agency as a fundamental organizing abstraction for computer science, extending beyond its current framing as merely an application of foundation models. The authors argue that the general-purpose, open-ended nature of agentic AI systems, capable of synthesizing actions from goals, observations, and memory across diverse tools and environments, necessitates a systems-level perspective. They identify structural abstraction gaps in areas like execution, interfaces, and oversight as the root cause of recurring failures in deployed agentic systems, rather than isolated engineering issues. AI

IMPACT Proposes a new conceptual framework for understanding and building AI systems, potentially influencing future research and development in agentic AI.

RANK_REASON The cluster contains an academic paper proposing a new conceptual framework for computer science.

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

Agency proposed as new organizing abstraction for computer science

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The cluster contains an academic paper proposing a new conceptual framework for computer science.
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COVERAGE [2]

  1. arXiv cs.AI TIER_1 English(EN) · Ian Foster ·

    Agency as a New Organizing Abstraction for Computer Science

    arXiv:2610.17410v1 Announce Type: new Abstract: Advances in large-scale machine learning have enabled agentic AI systems: persistent, autonomous computational entities that pursue goals, initiate actions, and interact with open environments. These systems are often framed as appl…

  2. arXiv cs.MA (Multiagent) TIER_1 English(EN) · Ian Foster ·

    Agency as a New Organizing Abstraction for Computer Science

    Advances in large-scale machine learning have enabled agentic AI systems: persistent, autonomous computational entities that pursue goals, initiate actions, and interact with open environments. These systems are often framed as applications of foundation models. We argue that thi…

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