Agensh: Scaling Coding Agent Teams Past a Thousand

This episode dives into Agensh, a Microsoft Research paper that scales leaderless AI coding-agent organizations up to 1,024 concurrent workers on the same codebase. The discussion frames why every current multi-agent coding framework—Claude Code's sub-agents, Codex, Copilot's fleet command, Kimi's Agent Swarm—hits the same wall: a single orchestrator agent that plans, assigns, and integrates work becomes the system's ceiling. Agensh removes that role entirely, replacing it with a five-step loop (gather context, claim, act, verify, merge) that every worker runs independently, coordinated only through shared infrastructure: a Gitea-backed shared workspace, a message interface, and a shared context log. The hosts debate whether removing top-down coordination actually prevents costly collisions or merely catches them after the fact, and examine the paper's ProgramBench results, including a jump from 33.89% to 55.06% test-pass rate on a real-world rebuild task when scaling from one agent to 1,024. Listeners interested in the practical limits of multi-agent orchestration and decentralized coordination at extreme scale will find the mechanics and skepticism here worth following.