AI-Agentic-Engineering-Roadmap

A comprehensive engineering roadmap to master AI Agents, RAG (Retrieval Augmented Generation), and Multi-Agent Systems.

This repository documents the journey from writing basic LLM scripts to building production-grade autonomous agent systems that can plan, reason, and execute tasks.

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🧠 Project Goal

To move beyond "Chatbots" and build "Agents". While chatbots just talk, Agents use tools, search the web, write code, and execute complex workflows. This repository serves as a portfolio of structured learning and building.

📂 Repository Structure

The project is organized into 4 high-level modules, progressing from infrastructure to deployment.

🔹 Module 01: Infrastructure & RAG

Focus: Tying LLMs to data and memory.

• 01_Python_APIs: Handling OpenAI/Anthropic API calls and structured JSON outputs.

• 02_Vector_DBs: Implementing semantic search (Pinecone/ChromaDB).

• 03_Basic_RAG: Building "Chat with PDF" using LangChain.

• 04_Advanced_RAG: Hybrid search, Re-ranking, and hallucination reduction.

🔹 Module 02: Single Agent Architectures

Focus: Teaching AI to use tools and reason.

• 01_Tool_Use: Function calling (Connecting LLMs to Calculators/APIs).

• 02_ReAct_Pattern: Implementing the "Reason + Act" loop.

• 03_Structured_Output: Enforcing strict JSON schemas with Pydantic.

• 04_Agentic_Memory: Managing long-term state with LangGraph.

🔹 Module 03: Multi-Agent Systems

Focus: Orchestrating a team of AI employees.

• 01_Agent_Patterns: Building "Software House" simulations (Coder + Reviewer).

• 02_Routing_Planning: Hierarchical task management & Supervisors.

• 03_Human_in_Loop: Approval workflows for critical actions.

• 04_Evaluation_Ops: Automated testing and evaluation datasets.

🔹 Module 04: Production & Deployment

Focus: The Flagship Portfolio Project.

• 01_FastAPI_Backend: Wrapping agents in robust REST APIs.

• 02_Frontend_UI: Building interfaces with Streamlit/Vercel.

• 03_The_Autonomous_Analyst: 🚩 Flagship Project (Data Analysis + Charting + Reporting).

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🛠️ Tech Stack

• Languages: Python

• LLMs: OpenAI (GPT-4), Anthropic (Claude 3.5)

• Orchestration: LangChain, LangGraph

• Vector DBs: Pinecone, ChromaDB

• Serving: FastAPI, Streamlit

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💻 How to Run This Code

Each module in this repository is self-contained. To run the code, please follow the specific instructions inside each module folder.

1. Clone the repository:

   
   git clone [https://github.com/Mustafa-Naqvi-110/AI-Agentic-Engineering-Roadmap.git](https://github.com/Mustafa-Naqvi-110/AI-Agentic-Engineering-Roadmap.git)
   
   cd AI-Agentic-Engineering-Roadmap
   
   

2. Set up Environment Variables:

   Create a .env file in your project root and add your API keys:

   
   OPENAI_API_KEY="sk-..."
   
   ANTHROPIC_API_KEY="sk-..."
   
   

3. Install Dependencies:

   Navigate to the specific module you are working on (e.g., 01_Infrastructure/01_Python_APIs) and install the requirements listed there.

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📈 Roadmap Phases

• Phase 1: Infrastructure

• Phase 2: Single Agents

• Phase 3: Multi-Agent Orchestration

• Phase 4: Deployment & Portfolio

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This roadmap follows the "AI Agentic Specialist" curriculum.