**[AI Agent Moving Towards Persistent Execution: X-Agent Explores Cross-Session Task Runtime Architecture]** BlockBeats News, September 16, according to official sources, X-Agent recently published an article titled *"Lessons from Muse: Exploring a Persistent Runtime for X-Agent"*, discussing the trend of AI Agents transitioning from instant interactions to long-term task execution. The article explores the next phase of system architecture: when tasks span hours or even days, how can an Agent save progress, wait for events, and resume execution after failures until the task is completed, even in the user's absence? The article proposes that a reliable Agent must store goals, tasks, execution records, events, and outputs within the system, leveraging a recoverable browser environment, MCP, and APIs to achieve persistent cross-session execution. Additionally, by implementing model access control, credential isolation, operation auditing, and high-risk action approvals, persistent execution can maintain clear safety boundaries. According to official sources, X-Agent summarizes this exploration as an evolution from *Agent Builder* to *Persistent Runtime*, *Controlled Runtime*, and *Learning Runtime*, emphasizing the measurement of practical outcomes through task completion rates, recovery success rates, and cost per task. The related architecture is still in the exploratory stage and is not yet an implemented feature or a formally committed roadmap. As AI applications accelerate their shift from "content generation" to "task execution," the ability to reliably and persistently complete real-world tasks has become the core focus of X-Agent's next phase. [Original Link]