The article examines how tool harness design impacts the post-training of large language model agents. It argues that while agents are routinely post-trained, the scaffolding determining tool exposure is often treated as a fixed detail. Existing algorithms typically assume static environments, ignoring shifts in tools and tasks during deployment. To address this gap, the authors extended ALFWorld to treat harness design as a controllable dimension. This extension supports evaluation under both task and tool environment shifts. The study systematically analyzes harness influence on post-training in in-distribution and out-of-distribution settings. Results show that harness-aware post-training improves performance and enables robust adaptation to new environments. Conversely, minimal design effort leads to drastic performance drops under strong environmental shifts.