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🤖 Agentic AI for Engineers - Build your own Financial Advisor with MCP
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🤖 Agentic AI for Engineers - Build your own Financial Advisor with MCP

06 March Online

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About this event

Retail investors and wealth management platforms are increasingly exploring AI-driven advisory tools to support portfolio construction, market analysis, and long-term investment decisions.
But building a reliable financial AI advisor requires far more than connecting an LLM to stock prices.

How do you design an Agentic AI system that can:

  • Retrieve and structure financial market data,
  • Reason over portfolio allocation and risk,
  • Maintain state and context across interactions,
  • And provide structured, explainable investment insights?

In this 60-minute interactive webinar, we’ll walk through the end-to-end design and implementation of an Agentic AI Financial Advisor, combining structured financial data with reasoning agents powered by LLMs.

The session includes a live implementation using Python, LangChain, OpenAI models, MCP protocol, and Yahoo Finance data, demonstrating how to orchestrate tools, memory, and reasoning steps into a coherent financial advisory system.

You’ll learn

💼 Core Concepts of Agentic AI in Finance
How agent-based systems differ from simple chatbots:

  • Tool use and financial data retrieval
  • Multi-step reasoning over market signals
  • State management across sessions
  • Guardrails and responsible AI considerations

🔗 Using MCP Protocol for Structured Agent Orchestration
How MCP enables:

  • Modular agent communication
  • Tool abstraction and extensibility
  • Separation of reasoning and execution layers
  • Scalable financial agent architectures

📈 Integrating Real Market Data (Yahoo Finance)
How to:

  • Retrieve live and historical market data
  • Structure time-series inputs for analysis
  • Perform portfolio-level reasoning
  • Generate explainable investment summaries

🤖 Live Demo: Building a Financial Advisor Agent
Step-by-step implementation:

  • Setting up LangChain with OpenAI
  • Connecting to Yahoo Finance APIs
  • Implementing MCP-based tool orchestration
  • Generating portfolio analysis and allocation suggestions
  • Producing transparent, plain-text financial explanations

🧠 From Data to Advice: Responsible AI & Limitations
Discussion around:

  • Risk, uncertainty, and hallucination management
  • Compliance considerations
  • Human-in-the-loop investment workflows
  • Where Agentic AI adds value in wealth management

📅 Duration: 60 minutes
🧠 Level: Intermediate (basic Python knowledge recommended)
🛠️ Tech Stack: Python, LangChain, OpenAI, MCP protocol, Yahoo Finance

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