Risk management in capital markets is becoming increasingly complex for investment portfolio managers due to the need to process vast amounts of data from real-time market feeds and unstructured social media data. AI agents, a type of artificial intelligence that can operate autonomously and take actions based on goals and real-world interactions, are set to transform how investment portfolios are managed. According to Gartner, 33% of enterprise software applications will include agentic AI by 2028, up from less than 1% in 2024. At least 15% of day-to-day work decisions are being made autonomously through AI agents. MongoDB empowers AI agents to effectively transform the landscape of investment portfolio management by leveraging the combination of large language models (LLMs), retrieval-augmented generation (RAG), and MongoDB Atlas Vector Search, enabling them to analyze vast financial datasets, detect patterns, and adapt in real time to changing conditions dynamically. This advanced intelligence elevates decision-making and empowers portfolio managers to enhance portfolio performance, manage market risks more effectively, and perform precise asset impact analysis. Intelligent investment portfolio management involves selecting, balancing, and monitoring a mix of financial assets to achieve a higher return on investment (ROI) while managing risk effectively and proactively. It requires the integration of diverse unstructured alternative data like financial news, social media sentiment, and macroeconomic indicators, alongside structured market data such as price movements, trading volumes, index, spreads, and historical execution records. The complex data integration presents a new level of sophistication in portfolio analytics, requiring a flexible, scalable, unified data platform that can efficiently store, retrieve, and manage diverse datasets. MongoDB’s flexible schema accelerates data ingestion across various data sources, including real-time market feeds, historical performance records, and risk metrics, supporting more intelligent decision-making and proactive market risk mitigation. AI-powered portfolio asset allocation has become a desirable characteristic of modern investment strategies, providing insights tailored to specific financial objectives and risk tolerances. This approach optimizes portfolio construction by recommending an alternate mix of assets while continuously assessing the evolving market conditions. Agentic AI foundation incorporates an orchestrator layer that manages task execution in workflows, enabling AI agents to operate fully autonomous or semi-autonomous with a human-in-the-loop (HITL). AI agents are equipped with advanced tools, models, memory, and data storage, leveraging both long and short-term contextual data for informed decision-making and continuity of interactions. The solution includes a data ingestion application, three AI Agents, and a market insight application that work in harmony to create a more intelligent, insights-driven approach to portfolio management. By leveraging AI agents and MongoDB, financial institutions can unlock the full potential of AI-driven portfolio management to obtain advanced insights that allow them to stay ahead of market shifts, optimize investment strategies, and manage risk with greater confidence.