[
    {
        "id": "osp-23285",
        "type": "article-journal",
        "title": "Agensh: Scaling Organizational Intelligence to 1,024 Agents",
        "author": [
            {
                "family": "Zhan",
                "given": "Zhihao"
            },
            {
                "family": "Song",
                "given": "Ting"
            },
            {
                "family": "Dong",
                "given": "Li"
            },
            {
                "family": "Huang",
                "given": "Shaohan"
            },
            {
                "family": "Lian",
                "given": "Jianxun"
            },
            {
                "family": "Xia",
                "given": "Yan"
            },
            {
                "family": "Wei",
                "given": "Furu"
            }
        ],
        "URL": "https://omanscience.com/ar/articles/agensh-scaling-organizational-intelligence-to-1-024-agents",
        "language": "en",
        "issued": {
            "date-parts": [
                [
                    2026
                ]
            ]
        },
        "abstract": "A multi-agent system can reduce latency on complex tasks by executing work concurrently. Several pioneering harness frameworks support multi-agent systems. However, the scalability of current multi-agent harnesses is often constrained by a central orchestrator's capacity to allocate tasks and coordinate workers. To address this limitation, we introduce Agensh, a scalable self-organized multi-agent harness without a central orchestrator: concurrent workers execute a multi-agent cooperation loop, continuously gathering context, claiming and self-assigning sub-tasks, taking action and sharing findings, verifying results, and merging progress in an asynchronous manner. The loop is supported by the agentic organization infrastructure comprising three components: a shared workspace holds proposed, ongoing, and completed work; a message interface lets workers communicate; and shared context retains reusable findings and work intentions. To test the scalability of Agensh, we evaluate it on the five hardest ProgramBench tasks with GPT-5.6-sol (high). Scaling from 1 to 128 agents raises the mean final test-pass rate from 19.31% to 28.78%, an approximately 49% relative improvement. Larger organizations reach comparable test-pass rates earlier. On pandoc, scaling from 1 to 1,024 agents raises the final test-pass rate from 33.89% to 55.06%. Worker trajectories further show that different forms of self-organized cooperation gradually emerges and standardizes as the organization grows. These results reveal the number of agents as a new scaling dimension for multi-agent organizations to expand the frontier of general intelligence, offering a practical solution for complex tasks under hard latency constraints or time budgets."
    }
]