Tool-integrated LLM agents are often wrapped within a *harness*: the scaffolding that determines which tools are exposed, how they are described, and what auxiliary information accompanies each per-step observation. While agents are routinely post-trained, this scaffolding is typically treated as a *fixed* engineering detail, with design effort limited to the training-free regime. Moreover, existing post-training algorithms assume a static environment, even though tool environments and tasks often shift upon deployment. To address this gap, we extend ALFWorld (i) to treat the harness as a controllable design dimension and (ii) to support evaluation under task and tool environment shifts. Building on this, we systematically analyze how the harness design influences post-training in both in-distribution and out-of-distribution (OOD) settings. We empirically show that *harness-aware post-training* not only improves in-distribution performance but also enables agents to robustly adapt to OOD settings. Under a harness with minimal design effort, post-training suffers a drastic performance drop under stronger tool environment shifts, further highlighting the importance of harness-aware post-training under such shifts.

In this paper, we propose ChronoBias, a novel benchmark for evaluating time-conditional group bias in the time-sensitive knowledge of large language models (LLMs).Our benchmark is constructed via a template-based semi-automated generation method, balancing the quality-quantity trade-off in existing benchmark curation approaches.For knowledge that changes over time, time-conditional group bias exhibits varying patterns across time intervals, evident in both the best- and worst-performing groups and in the bias metric itself.In addition to parametric knowledge bias–which influences group bias across all time intervals–we identify time-sensitivity bias as an additional factor after a model’s knowledge cutoff, accounting for much of the variation in time-conditional group bias over time.Since both biases are irreducible, retrieval-augmented generation (RAG) can be a promising approach, as it can address post-cutoff knowledge and better leverage pretraining knowledge that is underrepresented in the model parameters.While RAG improves both overall performance and group bias, we observe that the disparate patterns of time-conditional group bias still persist.Therefore, through extensive experiments with various model configurations, we illustrate how accurate and fair RAG-based LLMs should behave and provide actionable guidelines toward constructing such ideal models.