Software Engineering (SE) agents have shown promising abilities in supporting various SE tasks. Current SE agents remain fundamentally reactive, making decisions mainly based on conversation history and the most recent response. However, this reactive design provides no explicit structure or persistent state within the agent's memory, making long-horizon reasoning challenging. As a result, SE agents struggle to maintain a coherent understanding across reasoning steps, adapt their hypotheses as new evidence emerges, or incorporate execution feedback into the mental reasoning model of the system state. In this position paper, we argue that, to further advance SE agents, we need to move beyond reactive behavior toward a structured, state-aware, and execution-grounded reasoning. We outline how explicit structure, persistent and evolving state, and the integration of execution-grounded feedback can help SE agents perform more coherent and reliable reasoning in long-horizon tasks. We also provide an initial roadmap for developing next-generation SE agents that can more effectively perform real-world tasks.

翻译：软件工程（SE）智能体在支持各类软件工程任务方面已展现出潜力。当前的SE智能体本质上仍属于反应式系统，其决策主要基于对话历史和最新响应。然而，这种反应式设计未在智能体内部建立明确的结构化记忆或持久化状态，导致其难以进行长程推理。因此，SE智能体难以在推理步骤间保持连贯的系统理解、无法根据新证据调整假设，亦不能将执行反馈纳入系统状态的心理推理模型。在本立场论文中，我们认为要推动SE智能体的进一步发展，必须超越反应式行为，转向结构化、状态感知与执行驱动的推理范式。我们阐述了显式结构、持久化且可演化的状态以及执行反馈的融合机制，如何帮助SE智能体在长程任务中实现更连贯可靠的推理。同时，我们提出了开发下一代SE智能体的初步路线图，以更有效地执行现实世界任务。