Make Your Own Financial Agent for Investors

Agenda Overview
Section 1: Agent Architectures:

• Understand basic agentic AI concepts: single vs. multi-agent systems.
• Explore ReAct (Reason-Act-Observe) loops for decision-making and function APIs for data access.
• Demonstration: Use a LangGraph agent to query Yahoo Finance for cap table stress-tests.
• Assignment: Customize a GitHub template for your investment sectors.
• Demystify AI agents — break down LLMs, memory, tools, and the Observe → Think → Act loop.
• Compare architectures — monolithic vs. multi-agent, and heavyweight frameworks vs. minimalist harnesses.
• Explore pi-mono — a "primitives, not features" open-source toolkit for building custom agents.
• Build live — code a real VC due diligence research agent from scratch in the final session.
  Section 2: Automated Deal Sourcing:
• Create agents for lead generation using vector embeddings on Crunchbase/LinkedIn data.
• Discuss prompt engineering to evaluate founder teams.
• Hands-on integration: Combine Airtable with OpenAI agents for real-time matching; assess performance using precision and recall metrics.
  Section 3: Advanced Due Diligence
• Implement hierarchical agents for financial analysis and risk management.
• Focus on memory storage for audit trails and human oversight.
• Demo: Backtest your portfolio for performance improvements; homework involves a diligence simulation on live data.
  Section 4: Portfolio Agents & Scaling
• Coordinate multi-agent systems for monitoring, exit signals, and rebalancing.
• Discuss costs, addressing errors, and deployment methods.
• Capstone project: Collaboratively build a "VC Vault Agent"