AgenticAI- Development_Deployment_Cost Pricing MCP Server: Data-Driven C...

Request For Proposal Agentic AI With New Development_Deployment_Estimate_Effort_Cost MCP Server : Data-Driven Cost & Effort Estimation For Finalizing Proposal

Welcome to Part 3 of our deep dive into building a real-world Agentic AI system! In the previous parts, we built a powerful "AI consultant" that can analyze an RFP and iteratively refine a technical proposal based on user feedback. Now, we're adding the most critical feature for any serious business proposal: automated cost and effort estimation.

Join me as we upgrade our agent from a Technical Architect to a Pre-Sales Lead. We'll teach it how to create a Work Breakdown Structure (WBS), estimate development effort, and query a live data source for real-time cloud pricing to generate a complete financial proposal. This is where agentic AI moves from being a helpful tool to a true force multiplier for business operations.

🚀 What You'll Learn in THIS Video (Part 3 - Effort & Cost Estimation):

  1. Added new MCP server with Modified Prompt to 

     - Calculate Development Effort in Person days & Calculate Cost based on Effort

- Calculate Deployment Cost based on Identified Cloud Resources needed for deploying Solution

- Select Which Cloud Provider (Cloud providers like AWS, Azure, GCP, DigitalOcean) will be right choice along with Region

  2. Creating a Data-Driven Tool: We'll build a new cloud_pricing_server from scratch. Explained how it loads and parses a CSV file, turning it into a queryable "source of truth" for the agent.

  3. Advanced Task Decomposition: Agent's advanced reasoning as it breaks down the "estimate costs" request into a logical sequence: breakdown -> estimate effort -> estimate resources -> lookup prices -> calculate -> format output

     - Reading the existing proposal for context.

     - Making multiple, sequential calls to the pricing tool to gather data for each architectural component.

     - Synthesizing the results into a final financial table.

  4. Complex Prompt Engineering: We'll upgrade the "deep dive" prompt to teach the agent this new, multi-step financial analysis workflow.

  5. End-to-End Demo: See a complete, working demonstration of the agent progressing from a v1 technical document to a final v4 document that includes a detailed and accurate financial proposal.

  

🔧 Tech Stack & Libraries Covered:

    - AI Model: Google Gemini-2.5-Pro With Fallback model Gemini-2.5-flash

    - Agent Framework: Custom-built using Python & MCP-FastMCP

    - UI: Gradio (with gradio.State)

    - New Tool: A CSV-parsing Cloud Pricing Server

    - Core Libraries: csv, pathlib, asyncio, asyncio, aiofiles, python-dotenv

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

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

Thank you for following this advanced agent-building series! If you're excited about creating AI that can handle complex business logic, please like, subscribe, and hit the notification bell. Your support is incredibly appreciated!

#AgenticAI #GenerativeAI #Python #GoogleGemini #ArtificialIntelligence #CostEstimation #ConversationalAI #LLM #MCP #AI