Enter the Agentomaly: How Billions of Personal AI Agents Will Become the New Economy
Persistent personal AI agents may change the relationship between an individual's attention and productive capacity. This paper defines Agentomaly as a proposed transition in which individuals can affordably sustain adaptive, concurrent delegated work that previously required more direct attention or organizational support. Delegation and automation are not new. The hypothesis concerns a change in their accessibility, flexibility, persistence, and coordination cost at the level of the individual principal. The paper distinguishes three questions: whether agents expand individual productive capacity; whether their interactions become a substantial operational layer of the economy; and whether the resulting systems preserve meaningful human authority. These outcomes need not coincide. A closed platform could deliver large productivity gains while concentrating control and making exit difficult. The proposed analytical unit is the principal-agent relationship. Its performance should be evaluated through quality-adjusted completed outcomes, principal attention, total human labor, elapsed time, resource cost, and externalities. More agent activity is not evidence of more economic value. The paper also develops the Principal Concentration Problem: many agent identities may represent one controlling organization rather than independent market participants. A governance framework addresses bounded mandates, audit, continuity, portability, and human re-entry. These are design commitments to assess separately from productivity, not assumed consequences of technical capability. A research agenda proposes comparisons with manual work, AI-assisted work, conventional automation, and human delegation. The 2030 horizon is a scenario boundary, not a forecast. The contribution is an integrated framework for testing when persistent delegation expands individual agency, how its benefits and costs are distributed, and whether its expansion remains subject to effective human control.
Authors
- Facundo Barrera
Publication Details
- Journal
- Zenodo (CERN European Organization for Nuclear Research)
- Published
- 2026-09-30
- DOI
- https://doi.org/10.5281/zenodo.23059894
- Primary Topic
- Human-Automation Interaction and Safety
- Type
- article
- Field-Weighted Citation Impact
- 0.00