Microsoft's Agensh scales to 1,024 agents, lifting pandoc pass rate to 55%

MicrosoftResearch · hf · 2026-09-23

Microsoft Research introduced Agensh, a self-organized multi-agent harness with no central orchestrator, exploring agent count as a new scaling dimension.

Motivation: existing multi-agent harnesses are often bottlenecked by a central orchestrator's capacity to allocate tasks. Agensh instead runs a cooperation loop where concurrent workers gather context, self-assign subtasks, act, share findings, verify, and merge progress asynchronously.
Infrastructure: three components—a shared workspace for proposed/ongoing/completed work, a message interface for worker communication, and shared context retaining reusable findings and intentions.
Results: on the five hardest ProgramBench tasks with GPT-5.6-sol (high), scaling from 1 to 128 agents raised mean test-pass rate from 19.31% to 28.78% (49% relative improvement); larger organizations reach comparable rates earlier.
On pandoc, scaling from 1 to 1,024 agents lifted the pass rate from 33.89% to 55.06%. Worker trajectories show self-organized cooperation forms emerging and standardizing as the organization grows.

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Microsoft's Agensh scales to 1,024 agents, lifting pandoc pass rate to 55%

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