Agentic AI Architectures for Next-Generation Investment Advisory and Portfolio Intelligence

Abstract

Next-generation investment advisory systems will be able to autonomously support both retail and institutional clients in decision-making and portfolio oversight, while allowing for varied levels of interventions as warranted. To fulfill these dual roles and augment traditional investment teams, the transparency and consistency of agency architectures are essential. Transparent reasoning, evidence and experience grounds for decisions, and support in decision facilitation are the desired outcomes for and core capabilities of an investment agentic architecture. Modularity and interoperability across levels and domains will enable personalized advisory services and integrated investment advisory intelligence. Core capabilities of agentic systems—autonomy, goal-directed action, learning and adaptation, reasoning, and interaction—are employed to assess the state-of-the-art in investment advisory and related research, identify investment decision-based profiling variables, and explore the technology layers necessary for such advisory decision support along the lines of next-generation group and private banks. The focus is on agentic systems in the context of investment advisory practice and cyber-agents advising across sectors based on sound business principles, rather than defining the agentic implications of all information systems or agency in the sense of moral responsibility.