Self-Harness lets agents self-optimize scaffolding

While self-correcting agent scaffolding is a crucial step towards fully autonomous AI systems, its prompt-centric optimization acts as a patch rather than a fundamental cure for underlying model limitations.

* Removes Human Bottleneck: Eliminates the tedious, model-specific manual prompt engineering required to adapt generic agent scaffolds to specific LLMs.

* Safety via Validation: The integration of a regression testing step ensures that modifications to prompts or tool guidelines do not introduce catastrophic regressions in general capabilities.

* Prompt-Bound Boundaries: Because it works purely at the scaffolding/prompt level, the improvement upper bound remains constrained by the capabilities of the frozen underlying base model.

* Risk of Environment Overfitting: Constant validation against a specific benchmark suite might lead the agent to overfit its instructions to those test scenarios rather than learning generalizable skills.