A study evaluates the effectiveness of individual components in coding harnesses for AI agents. The researchers found that context management becomes more valuable as the context-window budget tightens, and that staging rule-based elision before LLM-based summarization provides the strongest efficiency among context-management strategies. The study also found that planning shifts from an accuracy scaffold for weaker models to a cost saver for stronger models. These findings inform model- and budget-aware harness design and provide a modular framework for evaluating future harness components.