Show HN: Agentsh – A Decentralized, Scalable Multiagent Harness

Microsoft open-sourced Agensh, a decentralized multi-agent harness that scales to 1,024 agents without a central orchestrator, on 2026-10-24 alongside experiment launch scripts and a reproduction guide. On the five hardest ProgramBench tasks using GPT-5.6-sol at high reasoning effort with a six-hour budget and no Internet access, the mean final test-pass rate rose from 19.31% with 1 agent to 28.78% with 128 agents, a roughly 49% relative improvement; on Pandoc the rate rose from 33.89% with 1 agent to 50.94% with 128 agents and 55.06% with 1,024 agents. With GPT-6.1-sol at high reasoning effort, the six-hour Pandoc test-pass rate rose from 56.19% with 1 agent to 79.59% with 32 agents, a 41.64% relative improvement.

🚀 2026-10-24: We open-sourced Agensh , including the harness, experiment launch scripts, and reproduction guide https://github.com/microsoft/Agensh/blob/main/REPRODUCE.md . 📢 2026-09-22: We published the Agensh preprint https://arxiv.org/abs/2609.26781 on arXiv. Agensh is a self-organized multi-agent harness that scales to 1,024 agents. Agents discover and claim sub-tasks, share findings, verify results and merge progress asynchronously, without a central orchestrator. A shared workspace, message interface and shared context connect each agent's local progress to the team. We evaluated Agensh on the five hardest ProgramBench tasks using GPT-5.6-sol with high reasoning effort, a six-hour budget and no Internet access. Agents rebuild reference programs from behavioral observations and bundled documentation. Across the 5 evaluated tasks, the mean final test-pass rate rises from 19.31% with 1 agent to 28.78% with 128 agents , an improvement of approximately 49% relative . Larger teams also reach comparable pass rates sooner. On Pandoc , the final test-pass rate rises from 33.89% with 1 agent to 50.94% with 128 agents and 55.06% with 1,024 agents under the same six-hour budget. We find that self-organized peer coordination, integration workflows and specialized roles gradually emerge as the team grows. With the latest GPT-6.1-sol at high reasoning effort, the six-hour Pandoc test-pass rate rises from 56.19% with 1 agent to 79.59% with 32 agents , a relative improvement of 41.64% . Agensh defines the multi-agent cooperation loop. Workers concurrently gather context, claim sub-tasks, take action, verify results and merge progress in a self-organized manner, repeating until the user’s goal is achieved. All workers proceed asynchronously and share progress through the infrastructure. Gitea https://about.gitea.com/ provides the shared workspace, Mattermost https://mattermost.com/ provides the messaging interface, and the shared context mechanism is adapted from DeLM https://github.com/yuzhenmao/DeLM . In our experiments, we integrate Agensh on top of the per-agent agentic loop of Copilot harness https://github.com/github/copilot-cli . This release includes a single-node team setup using Docker Compose and a standalone single-agent baseline, both using native Copilot ACP sessions. - Reproduction guide https://github.com/microsoft/Agensh/blob/main/REPRODUCE.md : prerequisites, configuration, image builds, launch commands, evaluation, recovery and release differences. - Repository layout https://github.com/microsoft/Agensh/blob/main/REPRODUCE.md repository-layout : component and folder guides. - Team launcher https://github.com/microsoft/Agensh/blob/main/scripts/launch single node.sh and single-agent launcher https://github.com/microsoft/Agensh/blob/main/scripts/launch single agent.py : experiment entrypoints. @article{zhan2026agensh, title = {Agensh: Scaling Organizational Intelligence to 1,024 Agents}, author = {Zhan, Zhihao and Song, Ting and Dong, Li and Huang, Shaohan and Lian, Jianxun and Xia, Yan and Wei, Furu}, journal = {arXiv preprint arXiv:2609.26781}, year = {2026}, url = {https://arxiv.org/abs/2609.26781} } Code is licensed under the MIT License https://github.com/microsoft/Agensh/blob/main/LICENSE . See the contribution guidelines https://github.com/microsoft/Agensh/blob/main/REPRODUCE.md contributing , security policy https://github.com/microsoft/Agensh/blob/main/SECURITY.md , support information https://github.com/microsoft/Agensh/blob/main/REPRODUCE.md security-and-support , and trademark guidance https://github.com/microsoft/Agensh/blob/main/REPRODUCE.md trademarks .