Generative AI, RAG, LangChain, LangGraph and Multi Agent AI Systems explained

Experienced software professionals transitioning into AI Engineer or AI Architect roles often learn how to use Generative AI tools but struggle to explain how enterprise AI systems are actually designed.

Modern enterprise AI systems are not built using standalone LLM prompts.

They are built using:

• RAG Architecture
• Embeddings
• LangChain pipelines
• LangGraph workflows
• Multi Agent AI Systems
• Agentic AI Architectures like Supervisor and Planner–Executor

This article explains how Generative AI evolves into Agentic AI architecture in enterprise systems and how concepts like embeddings, RAG, LangChain, LangGraph and CrewAI work together to build autonomous AI workflow systems.

What is Generative AI in Enterprise Software?

Generative AI refers to systems that can create:

• Text
• Code
• Summaries
• Decisions
• Structured outputs

using Large Language Models (LLMs) trained on massive datasets.

Unlike traditional Machine Learning models that predict outcomes, Generative AI generates new content based on context understanding.

At its core:

Previous Tokens + Context
        ↓
Probability Distribution
        ↓
Next Most Likely Token

This probabilistic prediction leads to:

• Code generation
• Requirement summarization
• Document analysis
• Query generation
• API explanation

However, standalone LLMs cannot access enterprise data or execute workflows reliably — which leads to the need for RAG architecture.

Role of Machine Learning in GenAI Systems

Traditional Machine Learning still plays a crucial role in enterprise AI systems.

ML models are responsible for:

• Detection
• Classification
• Forecasting
• Recommendation

Example enterprise pipeline:

Drone Video
    ↓
Object Detection ML Model
    ↓
Detected Persons Count
    ↓
LLM
    ↓
Report / Explanation

ML extracts facts
LLM interprets facts

Agentic AI combines both into decision-making systems.

What is RAG Architecture and How it Works

RAG (Retrieval Augmented Generation) enables LLMs to respond using enterprise-specific knowledge instead of only pre-trained data.

Instead of:

User → LLM → Response

Enterprise RAG Flow:

RAG Architecture is used in:

• Enterprise knowledge bots
• ITSM automation
• Legal document search
• Policy compliance systems

How Embeddings Work in Vector Databases

Embeddings convert text into numerical vectors representing semantic meaning.

Example:

"Account balance is low"
→ [0.231, -0.445, 0.981 …]

Similar sentences like:

"Balance insufficient"

are stored nearby in multi-dimensional vector space.

This enables:

• Semantic search like cosine similarity
• Document similarity
• Context retrieval
• Recommendation

LangChain – LLM Application Orchestration

LangChain helps developers build AI applications by:

• Prompt templating
• Tool integration
• Memory handling
• RAG pipelines
• API calling

Typical LangChain Pipeline:

User Input
   ↓
Prompt Template
   ↓
Retriever
   ↓
LLM
   ↓
Output Parser
   ↓
Structured Response

LangChain is suitable for:

• AI chatbots
• Document summarizers
• Resume analyzers

But managing complex workflows becomes difficult. Here is an example of a simple application using LangChain.

LangGraph – AI Workflow Orchestration Engine

LangGraph allows developers to design:

• Multi step reasoning
• Stateful agents
• Conditional execution
• Retry logic
• Tool selection

LangGraph Execution Model:

Planner Node
   ↓
Tool Executor
   ↓
Memory Update
   ↓
Reviewer Node
   ↓
Response Generator

---

LangChain vs LangGraph – When to Use What

Requirement	Use
Simple AI chatbot	LangChain
RAG Knowledge Bot	LangChain
Multi Step Workflow	LangGraph
Agent Coordination	LangGraph
Long Running Tasks	LangGraph

LangChain = LLM Service Layer
LangGraph = AI Workflow Engine

CrewAI and Multi Agent AI Systems

CrewAI enables multiple AI agents to collaborate as a team.

Each agent has:

• Role
• Goal
• Tools
• Memory

Example Multi Agent Travel Planner:

Research Agent → Hotel Search
Pricing Agent → Price Check
Review Agent → Sentiment Analysis
Planner Agent → Itinerary Creation

Used in:

• Enterprise automation
• Financial analysis
• Research workflows

Agentic AI Architectures Explained

Agentic AI systems perform:

• Planning
• Tool selection
• Execution
• Review

without human intervention.

Supervisor Architecture

Central controller assigns tasks.

Planner – Executor Pattern

Planner decides steps
Executor performs actions

User Input
   ↓
Planner Agent
   ↓
Task Breakdown
   ↓
Executor Agent
   ↓
API / DB Calls
   ↓
Reviewer Agent

Used in:

• DevOps automation
• Compliance audit
• Infrastructure provisioning

Enterprise Use Cases of Agentic AI

Agentic AI Architecture is used in:

• Automated ITSM ticket resolution
• Code migration tools
• Document compliance validation
• Customer onboarding automation
• Travel planning systems
• Fraud detection analysis

Popular LLM Models Used in Enterprise

Model	Provider	Capability
GPT-4o	OpenAI	Balanced reasoning
Claude	Anthropic	Long context
Gemini	Google	Multimodal
Llama	Meta	Open source
Mistral	Mistral AI	Lightweight

Pricing References:

OpenAI → https://platform.openai.com/pricing
Anthropic → https://www.anthropic.com/pricing
Google → https://ai.google.dev/pricing
AWS Bedrock → https://aws.amazon.com/bedrock/pricing

How This Helps in AI Engineer / Architect Interviews

Interviewers often expect candidates to explain:

• RAG Architecture
• Embeddings
• Agent Workflow
• LangChain vs LangGraph
• Multi Agent AI Systems

You can explain Agentic AI like:

Planner Agent → Creates Plan
Executor Agent → Calls Tools
Reviewer Agent → Validates Output
Supervisor Agent → Final Response

Conclusion

Enterprise AI systems are evolving from:

Rule Based Automation
→ ML Prediction
→ Generative AI
→ Agentic AI Architecture

Future AI Architects will not just write prompts —
they will design autonomous AI workflow systems.

Frequently Asked Questions (FAQs)