Agon: An Autonomous Large-Scale Omnidisciplinary Research System Built on Prompt Economy
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Computer Science > Software Engineering
Title:Agon: An Autonomous Large-Scale Omnidisciplinary Research System Built on Prompt Economy
Abstract:Large language models are making research production scalable, shifting the bottleneck from producing artifacts to judging claims. We present \textsc{Agon}, a research orchestrator that validates what can be checked inside the workflow and leaves the remaining judgments to human scientists. \textsc{Agon} is built on six design principles: Prompt Economy, Future-Facing, Minimal Prompts, OmniDisciplinary, Massive Parallelism, and Zero-Code. We ran \textsc{Agon} across domains for 444 iterations of Prompt Economy loops, using only small starting topics and no human-written experimental code. These deployments demonstrate scalability while exposing new classes of failure. We organize these failures into a taxonomy along severity, fixability, visibility, and capability locus. The taxonomy separates failures the loops can see and fix from those that require human judgment. Together, these results show that \textsc{Agon} is pushing research toward a new paradigm: machine scales, human steers.
| Subjects: | Software Engineering (cs.SE); Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Multiagent Systems (cs.MA) |
| Cite as: | arXiv:2606.24177 [cs.SE] |
| (or arXiv:2606.24177v1 [cs.SE] for this version) | |
| https://doi.org/10.48550/arXiv.2606.24177
arXiv-issued DOI via DataCite (pending registration)
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