Banger paper from Microsoft Research.

(bookmark it)

They run 1K+ coding agents at once to test a scalable self-organized multi-agent harness.

This is an interesting test because most multi-agent systems today have some hierarchy or structure.

Agensh has no central orchestrator.

It coordinates parallel coding agents through a shared state instead of a central orchestrator.

Each agent gathers context, claims a sub-task, does the work, shares what it found, verifies the result, and merges it, all asynchronously through a shared workspace and a message channel.

On the five hardest ProgramBench tasks with GPT-5.6-sol, increasing agents from 1 to 128 raises the mean final test-pass rate from 19.31% to 28.78%.

Larger teams also reach a given pass rate sooner.

On pandoc, 1,024 agents take the test-pass rate from 33.89% to 55.06%.

The authors also report forms of cooperation that the agents start on their own and that become standard practice as the team grows.

Paper: https://arxiv.org/abs/2609.26781

Chat with Paper: https://academy.dair.ai/papers/agensh-scaling-organizational-intelligence-to-1-024-agents-2609.26781