ACCORD: Action-Conditioned Contextual Grounding for Language Agents

User instructions are often underspecified because humans rely on implicit assumptions about the surrounding environment. For large language model (LLM) agents operating in information-rich digital and physical environments, these assumptions cannot be inferred from the instruction alone; they must be recovered from the current state of tools, data, interfaces, and observations. Effective execution therefore requires agents to identify missing context, ground it in observed evidence, and carry it forward into subsequent actions. We show that current agents often fail to do so. They act from assumed rather than observed specifics, overlook information they could have gathered, and fail to incorporate evidence that has already been returned. Building on this insight, we propose Accord (Action-Conditioned Contextual Grounding), a simple and effective agent framework for adaptive grounding. Before each action, Accord actively probes the environment for missing information and integrates relevant context from the agent's trajectory that would otherwise be overlooked. Requiring no additional training or task-success signals, Accord improves task-goal completion on AppWorld by up to $+20.6$ points with GPT-5-mini, from $42.0%$ to $62.6%$, compared to strong baselines. These gains persist with a substantially stronger base model ($+10.8$ with Claude-4.5-sonnet), an open-weight model ($+10.1$ with Qwen3.5-27B-FP8), and on the embodied AlfWorld benchmark ($+7.4$ success rate with GPT-5-mini).