Capstone Project: The Sovereign Analyst

Welcome to the final challenge. Over the last 15 modules, you have built the components of a digital brain. Now, you will assemble them into a single, cohesive Market Analyst Agent.

This agent will not just "Chat"; it will investigate companies, read their documents, search for current events, and provide a professional, structured investment summary.

---

1. Project Requirements

Your system MUST include the following components:

A. Document Ingestion (Module 5 & 6)

• The ability to load a PDF (e.g., an annual report) or a URL.
• Split the documents into optimized chunks.
• Store them in a persistent Chroma or FAISS database.

B. Agentic Reasoning (Module 9 & 10)

• The agent must have access to at least two tools:
  • Retriever Tool: To find facts in its local database.
  • Web Search Tool: (Tavily or DuckDuckGo) to find current metrics.
• The agent must use the ReAct loop to combine these sources.

C. Conversation State (Module 8)

• The agent must remember the user's previous questions using Persistent Memory.

D. Structured Output (Module 11)

• The final report must be a JSON object with the following fields:
  • company_name: string
  • summary: string (1 paragraph)
  • sentiment: enum (Bullish / Bearish / Neutral)
  • sources: list of strings

E. Production Wrapper (Module 14 & 15)

• Serve the agent via a FastAPI endpoint.
• Include Caching for search results.
• Provide a Dockerhfie for deployment.

---

2. The Architectural Blueprint

graph TD
    User[User Question] --> API[FastAPI Server]
    API --> Agent[Agent Executor]
    Agent --> Memory[(Memory Store)]
    Agent -->|Logic| Brain[LLM Reasoner]
    Brain -->|Tool Choice| T1[Vector DB Search]
    Brain -->|Tool Choice| T2[Web Search]
    T1 --> Data[Context]
    T2 --> Data
    Data --> Brain
    Brain -->|Output| Parser[Pydantic Parser]
    Parser --> Result[JSON Report]
    Result --> API
    API --> User

---

3. Implementation Steps

1. Step 1: Setup: Create your project structure, virtual environment, and .env file.
2. Step 2: Data: Ingest a sample business PDF into your vector store.
3. Step 3: Tools: Wrap your retriever and search engine as LangChain tools.
4. Step 4: Brain: Build the agent using create_openai_functions_agent (or equivalent).
5. Step 5: Surface: Create the FastAPI route and the with_structured_output schema.
6. Step 6: Test: Ask the agent: "Read the provided report for Vortex Inc., check their current stock price online, and give me a summary."

---

4. Deliverables

To complete the course, you should have:

• A GitHub repository containing the source code.
• A README.md with setup instructions.
• A sample output JSON report generated by your agent.

---

Final Congratulations

By completing this Capstone, you have proven that you can architect and deploy a complex, safe, and useful AI system. You are no longer just a "Prompt Engineer"—you are an Agentic AI Developer.

---

What's Next?

If you want to continue your journey into Multi-Agent systems, high-reliability state machines, and Human-in-the-loop governance, join our Course 3: Agentic AI Development (Advanced).

The future is automated, and you have just built the engine. 🚀