The evolution of AI agents is moving beyond simple Q&A into delegated, long-horizon task execution. This shift requires agents to maintain context, plan sub-tasks, and recover from failures without human intervention. The Chinese developer community is actively discussing these patterns, reflecting a global trend toward agentic workflows. For engineering leaders, the key takeaway is that building reliable long-running agents demands robust state management, clear task decomposition, and feedback loops. Startups should watch this space as infrastructure for agent orchestration matures, enabling new product categories around autonomous operations.
AI agents are shifting from conversational tools to autonomous workers capable of executing multi-step tasks over time. This signal explores the architectural shifts and practical hurdles in building such systems.