Agentic AI With MCP : Building an RFP Analyzer With Deep-Dive Analysis t...

Agentic AI With MCP : Building an RFP Analyzer With Deep-Dive Analysis to Finalize Proposals - Part2

Welcome to Part 2 of our project to build a real-world Agentic AI system! In Part 1, we created an incredible agent that could perform a "one-shot" analysis of an RFP and generate a complete high-level design document. Now, it's time to make it smarter, more interactive, and truly production-ready.

In this video, we evolve our RFP Analyzer from a static "generator" into a dynamic "AI consultant." We'll implement a stateful, conversational workflow that allows a user to ask for follow-up elaborations, iteratively refining the proposal until it's perfect. We also tackle a critical real-world problem: what happens when your API calls fail?

Join me as we implement the deep-dive analysis features with following OPTIONs, teach our agent how to handle different follow-up requests, and build in a robust fallback mechanism to handle API rate-limiting errors gracefully.

  1. Elaborate on Proposed Architecture

  2. Provide Deeper Comparison of Technology Stacks

  3. Detail the Cloud Deployment Services

  4. Generate Alternative Technology Stacks (.NET, Java, Python)

🚀 What You'll Learn in THIS Video Deep-Dive Analysis (Part 2):

  1. Stateful Conversational UI: How to use gradio.State to create a multi-step, interactive user experience where the application remembers the context of the previous steps.

  2. Agentic "Prompt Routing": Evolving the agent's think() method into an intelligent router that uses different, specialized prompts for initial analysis vs. deep-dive follow-up questions.

  3. Iterative Document Refinement: See how the agent can read the v1 document it created, add new, more detailed content to a specific section, and save it as a new v2 document.

  4. Advanced Agent Logic: We'll teach the agent how to handle different types of requests, from elaborating on architecture to completely replacing the technology stack section with new user-specified options.

  5. Production-Ready Error Handling: A complete walkthrough of how to catch the 429 ResourceExhausted (rate limit) error from the Gemini API and implement an automatic fallback to a secondary model (gemini-1.5-flash-latest) to ensure the task can complete.

  

🔧 Tech Stack & Libraries Covered:

  - Core Libraries: asyncio, aiofiles, python-dotenv

  - AI Model: Google Gemini 1.5 Pro & Gemini 1.5 Flash

  - Agent Framework: Custom-built using Python & the Model Context Protocol (MCP-FastMCP)

  - UI: Gradio (with gradio.State)

  - PDF/Document Handling: PyMuPDF, python-docx, WeasyPrint, markdown2

  - Error Handling: google.api_core.exceptions for rate-limiting

  - Core Libraries: asyncio, pathlib

Thank you for joining me on this advanced agent-building journey! If you're getting value from this series, please like, subscribe, and hit the notification bell. Your support helps the channel grow!