New research from Meta.

Agent harnesses are still mostly authored by hand.

This makes it hard to tune robust agent harnesses for long-horizon tasks.

In this new work, agents learn harness policies offline and deploy them to construct and update external harness state online during runtime task execution.

EvoHarness-RL learns that policy instead. Belief, Progress, and Experience are exposed as harness state the policy can act on.

Supervised harness fine-tuning teaches the action space, then cost-aware GRPO explores when to read, update, and consolidate during a long run. Qwen3-8B reaches 96.9% on ALFWorld.

Two dynamics come out of the training.

Harness annealing means recurring harness-use patterns get absorbed into the model policy, and the agent shifts from frequent calls toward selective access.

Harness evolution means progress updates and experience consolidation compress the workspace into a compact task-adaptive state.

This shows that long-horizon agents get more from a trainable coordination policy than from bigger tools or larger memories.

Paper: https://arxiv.org/abs/2608.05446

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