Possible complementary direction: LongHorizon-Harness for sustained agent tasks

Hi, I'd like to suggest a complementary direction for learn-claude-code.

Project:
https://github.com/AMAP-ML/LongHorizon-Harness

Your project focuses on an agent harness and orchestration layer.

LongHorizon-Harness focuses on carrying one task across many rounds without state drift. It keeps the goal and verified progress in durable state, starts each execution round with a fresh context, and uses an independent Auditor before accepting progress.

Why this may be useful here:

• A short workflow note could show how to avoid context drift and premature completion in a task that spans many rounds.
• it adds an outer task-lifecycle layer without claiming to replace the project's existing agent or runtime

If this direction fits, I can prepare a small documentation or example contribution following the repository's conventions.

I am part of the team behind LongHorizon-Harness, so I wanted to disclose that connection.

Paper:
https://arxiv.org/abs/2608.01964

Project page:
https://lh-harness.pages.dev

Review the repository's existing documentation and example conventions, then compare them with the proposed LongHorizon-Harness workflow. A contribution would be complete only after the project accepts a defined small documentation or example scope; no specific file or test is named in the issue.