mlsanityData to 7 Oct 2026

The Agent Use of Agent Beings: Agent Cybernetics Is the Missing Science of Foundation Agents

arXiv 2605.10754 ↗First posted 11 May 2026cs.AIcited by 0

On the map: LLM reasoning → Reasoning for agents, planning and code

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Abstract (arXiv)

LLM-based foundation agents that perceive, reason, and act across thousands of reasoning steps are rapidly becoming the dominant paradigm for deploying artificial intelligence in open-ended, long-horizon complex tasks. Despite this significance, the field remains overwhelmingly engineering-driven. Engineering practice has converged on useful primitives (tool loops, memory banks, harnesses, reflection steps), yet these are assembled by empirical trial and error rather than from first principles. Fundamental questions remain open: under what conditions does a long-running agent remain on-task? How should an agent respond when its environment exceeds its representational capacity? What architectural properties are necessary for safe self-improvement? We argue that cybernetics, the mid-twentieth-century science of control and communication in complex systems, provides the missing theoretical scaffold for foundation agents. By mapping six canonical laws of classical cybernetics onto six agent design principles, and synthesizing those principles into three engineering desiderata (reliability, lifelong running, and self-Improvement), we arrive at a framework termed Agent Cybernetics. Three application domains, code generation, computer use and automated research, exemplify the analytical framework of agent cybernetics by identifying failure modes and concrete engineering recommendations. We hope that agent cybernetics opens a new research venue and establishes the scientific foundation that foundation agents need for principled, reliable real-world deployment.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Reasoning for agents, planning and code, one of 17 directions in LLM reasoning. Builds on 34 of its 51 references that are in the map. First posted on arXiv 11 May 2026.

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34 of 51 references.

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Other references · 33

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  9. 2019Mastering Atari, Go, Chess and Shogi by Planning with a Learned Modelcited by 1,327
  10. 2024OSWorld: Benchmarking Multimodal Agents for Open-Ended Tasks in Real Computer Environmentscited by 1,134
  11. 2023On-Policy Distillation of Language Models: Learning from Self-Generated Mistakescited by 840
  12. 2015Deep Learning and the Information Bottleneck Principlecited by 793
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  11. 2025SimpleTIR: End-to-End Reinforcement Learning for Multi-Turn Tool-Integrated Reasoningcited by 145
  12. 2024Cradle: Empowering Foundation Agents Towards General Computer Controlcited by 114
  13. 2026AutoSkill: Experience-Driven Lifelong Learning via Skill Self-Evolutioncited by 105
  14. 2025OS-Harm: A Benchmark for Measuring Safety of Computer Use Agentscited by 104
  15. 2026Externalization in LLM Agents: A Unified Review of Memory, Skills, Protocols and Harness Engineeringcited by 68
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  21. 2026On the Reliability of Computer Use Agentscited by 8

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