Agensh: Scaling Organizational Intelligence to 1,024 Agents
Agensh lets up to 1,024 self-organized agents raise ProgramBench scores without a central orchestrator.
Agensh is a self-organized multi-agent harness that avoids a central orchestrator. Workers concurrently gather context, claim sub-tasks, act, share findings, verify results, and merge progress through a shared workspace, message interface, and shared context. On the five hardest ProgramBench tasks with GPT-5.6-sol (high), scaling from 1 to 128 agents raised the mean final test-pass rate from 19.31% to 28.78%. On pandoc, scaling from 1 to 1,024 agents raised the final test-pass rate from 33.89% to 55.06%.
- Agensh has no central orchestrator; workers claim and self-assign tasks asynchronously.
- Shared workspace, messaging, and shared context support the cooperation loop.
- With 128 agents, mean ProgramBench pass rate rose from 19.31% to 28.78%.
- On pandoc, 1,024 agents raised the pass rate from 33.89% to 55.06%.
- The evaluation used GPT-5.6-sol at the high setting.
- Products
- AgenshProgramBench
- AI models
- GPT-5.6-sol
From the original article
1 min read at arxiv.orgA 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.
By Zhihao Zhan · arxiv.org
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