AI-Agents: Automation & Business with LangChain & LLM Apps

A well-rounded, hands-on course for developers and entrepreneurs to build functional, secure AI agents and turn them into business-ready tools.

What will you in AI-Agents: Automation & Business with LangChain & LLM Apps Course

Build AI agents using frameworks like LangChain, LangFlow, Flowise, LangGraph, Autogen, BabyAGI, and CrewAI.
Leverage LLMs such as GPT‑4, Claude, Gemini, Llama 3, and Mistral with function calling capabilities.
Create RAG-enabled AI agents using vector databases, embeddings, and custom data preparation.

Develop automations for content generation, email campaigns, lead research, and integrating custom tools with APIs.
Learn business-focused skills: pricing AI solutions, marketing strategies, and deploying agents on websites or as standalone tools.
Understand AI security considerations: preventing prompt injections, handling privacy, and copyright compliance.

Module 1: AI Agent Fundamentals

⏳ 30 minutes

Overview of various AI agent frameworks: LangChain, LangFlow, LangGraph, Autogen, BabyAGI, CrewAI.
Introduction to key LLM models (ChatGPT, Claude, Gemini, Llama) and function calling.

Module 2: Tools, Vector DBs & RAG

⏳ 60 minutes

Set up vector databases and embeddings for content retrieval.
Train agents on custom data (PDFs, CSVs) using LlamaIndex, LlamaParse, integrating Flowise/Node tools.

Module 3: Building Agents & Automation

⏳ 75 minutes

Module 4: Flowise & Custom Integration

⏳ 60 minutes

Install and configure Flowise with Node.js environment.
Embed function-calling agents with Gmail, calculator, Serper, Microsoft Copilot, etc.

Module 5: Business Applications & Deployment

⏳ 60 minutes

Learn to deploy agents on websites or as standalone applications.
Develop marketing strategies, set pricing, handle customer acquisition and operations.

Module 6: Security, Compliance & Open-Source LLMs

⏳ 45 minutes

Best practices for guarding against prompt injection and data poisoning.
Use open-source LLMs like Ollama, Llama 3.1 and choose appropriate models for specific tasks.

High demand for AI engineers developing task automation and agent-driven solutions—especially in business domains.
Skills supported: LLM integration, prompt engineering, vector search, API connectivity, and toolchain automation.
Salary potential: $110K–$180K+ for developers building AI workflows and RAG-powered applications.
Freelance avenues: Developing and selling custom AI agents for marketing automation, research, customer service, etc.

9.7Expert Score

Highly Recommended

A business-focused, end-to-end course on building secure, multi-modal AI agents with up-to-date tools and monetization advice.

Value

9.3

Price

9.5

Skills

9.7

Information

9.6

PROS

Combines agent framework training with business strategy, deployment, and pricing models.
Covers open-source LLMs and local Copilot integration for practical, customizable solutions.
Security-focused modules ensure safe agent usage in real-world contexts.

CONS