Agentic AI: can we streamline economic policy briefing?

Central banks increasingly rely on large volumes of policy documents-ranging from monetary policy statements to regulatory guidelines and internal reports-to communicate decisions and maintain institutional accountability. These documents help ground decisions in the most up-to-date economic signals, support internal coordination and strengthen transparency toward markets and the public. Yet their complexity and frequency impose significant administrative and analytical burdens. The emergence of agentic AI, which combines large language models with autonomous goal setting, structured workflows and tool-calling capabilities, offers the potential to automate key tasks such as drafting, summarization, cross-document consistency checks, integration with data sources and the generation of tables and figures. In this paper, we address the question posed in the title by examining the role that recent advances in AI-enhanced by agent-based architectures and coordinated task execution-can play in central bank workflows. We present two simple, ground-up examples showing how open-source tools can be leveraged effectively while keeping development and operating costs under control. The paper concludes by discussing how agentic AI could reshape document processing practices within central banks, highlighting both the efficiency gains and the governance safeguards required for its trustworthy adoption.