mlsanityData to 14 Oct 2026

The Wealth of Agents: Scaling Agentic AI Across Developers

arXiv 2610.20518 ↗First posted 14 Oct 2026cs.AI cs.MAcited by 0

On the map: LLM agents and tool use → General LLM agent frameworks and benchmarks also fits: Software engineering coding agents

Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in General LLM agent frameworks and benchmarks, one of 16 directions in LLM agents and tool use. Builds on 29 of its 42 references that are in the map. First posted on arXiv 14 Oct 2026.

Builds on

29 of 42 references.

Closest ancestors · 13

Same direction, newest first

  1. 2026Can Small Agents Collaborate to Beat a Single Large Language Model?16 Jan
  2. 2025Towards a Science of Scaling Agent Systems9 Dec
  3. 2025Small Language Models are the Future of Agentic AI2 Jun
  4. 2025Position: Collaborative Agentic AI Needs Interoperability Across Ecosystems25 May
  5. 2025Single-agent or Multi-agent Systems? Why Not Both?23 May
  6. 2025X-MAS: Towards Building Multi-Agent Systems with Heterogeneous LLMs22 May
  7. 2025AgentNet: Decentralized Evolutionary Coordination for LLM-based Multi-Agent Systems1 Apr
  8. 2024Beyond the Sum: Unlocking AI Agents Potential Through Market Forces19 Dec
  9. 2024MorphAgent: Empowering Agents through Self-Evolving Profiles and Decentralized Collaboration19 Oct
  10. 2024On the Resilience of LLM-Based Multi-Agent Collaboration with Faulty Agents2 Aug
  11. 2024Internet of Agents: Weaving a Web of Heterogeneous Agents for Collaborative Intelligence9 Jul
  12. 2023MetaGPT: Meta Programming for A Multi-Agent Collaborative Framework1 Aug
1 more
  1. 2023CAMEL: Communicative Agents for "Mind" Exploration of Large Language Model Society31 Mar

Other references · 16

  1. 2022ReAct: Synergizing Reasoning and Acting in Language Modelscited by 8,695
  2. 2023Generative Agents: Interactive Simulacra of Human Behaviorcited by 3,568
  3. 2022Training Compute-Optimal Large Language Modelscited by 3,019
  4. 2023AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversationcited by 1,988
  5. 2023GAIA: a benchmark for General AI Assistantscited by 1,128
  6. 2023FrugalGPT: How to Use Large Language Models While Reducing Cost and Improving Performancecited by 853
  7. 2024RouteLLM: Learning to Route LLMs with Preference Datacited by 682
  8. 2025Why Do Multi-Agent LLM Systems Fail?cited by 604
  9. 2025Where LLM Agents Fail and How They can Learn From Failurescited by 133
  10. 2023Mindstorms in Natural Language-Based Societies of Mindcited by 107
  11. 2025SealQA: Raising the Bar for Reasoning in Search-Augmented Language Modelscited by 75
  12. 2025ToolOrchestra: Elevating Intelligence via Efficient Model and Tool Orchestrationcited by 47
4 more
  1. 2025Agentic Web: Weaving the Next Web with AI Agentscited by 45
  2. 2025LLM Economist: Large Population Models and Mechanism Design in Multi-Agent Generative Simulacracited by 22
  3. 2025Virtual Agent Economiescited by 21
  4. 2026State of AI: An Empirical 100 Trillion Token Study with OpenRoutercited by 18

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Not cited yet.

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