Abstract: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.
| Comments: | Preliminary Work |
| Subjects: | Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2605.10754 [cs.AI] |
| (or arXiv:2605.10754v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2605.10754 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Chang Yang [view email]
[v1]
Mon, 11 May 2026 15:53:54 UTC (1,850 KB)