AI Engineering Learning: From Zero to Multi-Agent Systems

The Comprehensive Roadmap to Building Production-Ready AI Applications

Status: Updated for 2026
Duration: 8 Weeks (Self-Paced)
Prerequisites: Python, Basic SQL

📋 Program Overview

Welcome to the AI Engineer Learning Path. This curriculum is designed for developers with basic Python knowledge who want to learn AI engineering fundamentals. You’ll progress from simple LLM interactions to building intelligent agents that can reason, use tools, and work together to solve complex problems.

What You Will Build

• Core Agentic Architectures: LangChain, LangGraph, and Tool Use.
• Cognitive Systems: Implement RAG (Retrieval Augmented Generation) and long-term memory persistence.
• Orchestration: Design multi-agent swarms capable of complex problem-solving.

🗺️ The Curriculum

Follow this progressive path. Each module connects to a deep-dive tutorial with code examples and architectural patterns.

Phase 1: Foundations

Module 1: LangChain Fundamentals

Timeline: Week 1-2

Focus: Core Concepts

Before building complex agents, you must master the atomic unit of LLM applications: the Chain. This module covers the essential plumbing required to build reliable AI features.

• Key Topics:
  • Environment Setup & Installation
  • Prompt Templates & Engineering
  • LangChain Expression Language (LCEL)
  • Streaming & Debugging Patterns
• Outcome: Build your first structured chat application.
• 👉 Start: LangChain Fundamentals

Module 2: RAG Implementation

Timeline: Week 2-3

Focus: Data Context

Learn to ground your AI in reality. We explore Retrieval Augmented Generation to connect LLMs to your private data, reducing hallucinations and increasing utility.

• Key Topics:
  • Vector Databases (FAISS, Pinecone)
  • Embedding Models & Strategies
  • Context Augmentation
  • Hybrid Search Techniques
• Outcome: Build a “Chat with your PDF” document processing system.
• 👉 Start: RAG Implementation

Phase 2: Agency & Logic

Module 3: Tool Integration

Timeline: Week 3-4 | Focus: Action

LLMs are just text engines until you give them hands. This module teaches you to bind functions (tools) to models, allowing them to interact with APIs, databases, and the web.

• Key Topics:
  • Binding Tools to LLMs
  • Argument Extraction & Validation
  • API Chain Execution
  • Handling Tool Outputs & Errors
• Outcome: Create a customer support assistant that can query order status APIs.
• 👉 Start: Tool Integration

Module 4: LangGraph Workflows

Timeline: Week 4-5

Focus: Control Flow

Move from linear chains to cyclic graphs. LangGraph allows you to define loops, conditional branches, and state management—essential for robust agent behavior.

• Key Topics:
  • State Definition (TypedDict)
  • Nodes & Edges
  • Conditional Logic & Branching
  • Human-in-the-loop patterns
• Outcome: Design a multi-step workflow for data validation and classification.
• 👉 Start: LangGraph Workflows

Phase 3: Advanced Architecture

Module 5: Memory Integration

Timeline: Week 5-6

Focus: Persistence

Production agents need to remember users across sessions. We dive into the complexities of short-term context windows versus long-term vector storage.

• Key Topics:
  • Short-term Conversation Buffers
  • Long-term Vector Memory
  • User Preference Extraction
  • LangMem Integration
• Outcome: Build a personalized assistant that recalls past interactions.
• 👉 Start: Memory Integration

Module 6: Multi-Agent Orchestration

Timeline: Week 6-8

Focus: Scale

The frontier of AI engineering. Learn to coordinate multiple specialized agents to solve problems too complex for a single prompt.

• Key Topics:
  • Supervisor & Manager Patterns
  • Hierarchical Teams
  • Handoff Protocols
  • Adaptive vs. Linear Orchestration
• Outcome: Architect a complex resolution system with specialized sub-agents.
• 👉 Start: Multi-Agent Systems

💡 Best Practices for Success

1. Code Along: Do not just read. Each module contains code blocks—run them locally.
2. Iterate: After Module 1, build a simple bot. After Module 2, give that bot a document to read. Keep layering complexity.
3. Reference: Use these guides as your primary tutorial, but keep the official LangChain documentation open for API updates.