Agentic Portfolio Construction: A Multi-Agent Architecture for LLM-Driven Financial Asset Allocation

Abstract

Accurately curating portfolios and managing asset allocation is an essential and complex task in financial risk management. The emergence of large language models (LLMs) has helped resolve this task to a good extent. Recent literature have demonstrated that LLMs alone remain insufficient. We introduce a multi-agent portfolio construction architecture that integrates LLMs with an agentic orchestration architecture to enhance financial asset allocation workflows. With LangGraph, the system orchestrates interactions between LLM-based agents and functional nodes, enabling adaptive decision-making. The pipeline begins with parsing natural language investment objectives, followed by real-time data retrieval, financial metric computation, and portfolio generation tailored to the user’s risk profile and constraints. The results not only addresses risk-aware investing but also that the final output includes validation and a human-readable commentary. This approach addresses key deficiencies in standalone LLMs, such as lack of real-time awareness, by coordinating modular agents that specialize in tasks like data retrieval, analysis, and reporting. The architecture demonstrates how multi-agent LLM systems can perform complex, high-stakes financial tasks with adaptability, transparency, and robustness, marking a promising direction for AI-driven portfolio management.1