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The Wealth of Agents: Scaling Agentic AI Across Developers — arXiv:2610.20518

Source: arXiv (en)
Original: https://arxiv.org/abs/2610.20518
License (as archived): CC0-1.0
Archived: 2026-10-15T13:50:48.183Z
SHA-256 of the archived text: 17f45feed6bb8fedf859f09b7e1d751a3b3fa6501379bd98b34f8b988eb3e14f
Stable URL of this page: https://metraipolis.com/documento/dc_7790aabbdaeb

How to cite

The Wealth of Agents: Scaling Agentic AI Across Developers — arXiv:2610.20518 — arXiv (en), https://arxiv.org/abs/2610.20518 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_7790aabbdaeb (Metraipolis archive dc_7790aabbdaeb, sha256 17f45feed6bb8fedf859f09b7e1d751a3b3fa6501379bd98b34f8b988eb3e14f)

BibTeX

@misc{dc7790aabbdaeb,
  title = {The Wealth of Agents: Scaling Agentic AI Across Developers — arXiv:2610.20518},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2610.20518},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_7790aabbdaeb; sha256 17f45feed6bb8fedf859f09b7e1d751a3b3fa6501379bd98b34f8b988eb3e14f},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2610.20518. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Today's agentic systems are typically designed, built, and deployed by single developers with full control over all participating agents. While this simplifies development, it limits the potential scale of agentic systems. We study multi-developer agentic systems (MDAs) which enable developers to collaboratively build agentic systems to push frontier capability beyond what any individual developer could achieve alone. We construct empirical and theoretical models of developers to compare MDAs with single-developer agentic systems (SDAs). On GAIA, a general assistant benchmark, a population of specialized developers whose individual agents do not exceed 35% accuracy reaches 70% when their agents are combined in an MDA. This MDA performs 12 percentage points above the median general-purpose SDA built with several times each specialist's development budget. Our theoretical models show how MDAs can incentivize specialization and encourage more efficient use of aggregated developer resources. Put together, our results make a preliminary case for MDAs as a new scaling axis by enabling developers with diverse capabilities and resources to advance agentic AI collaboratively.

Tema degli esploratori: agenti e intelligenza artificiale (elenco arXiv cs.MA, nuovi lavori del giorno).
Categorie arXiv: Artificial Intelligence (cs.AI) ; Multiagent Systems (cs.MA).
Pagina del lavoro: https://arxiv.org/abs/2610.20518 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-15T13:48:35.473Z dalla pagina https://arxiv.org/list/cs.MA/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).