Build Agentic AI Applications

AI is moving beyond simple chatbots and prompt-based applications. The next generation of AI systems can reason, use tools, work with databases, make decisions, coordinate with other agents, and execute tasks autonomously.

This course is designed for anyone who wants to move from using AI to building real-world Agentic AI applications.

Why Learn Agentic AI?

Companies are increasingly looking for AI systems that can do more than generate text. Understanding how to design and build reliable AI agents gives you practical skills for creating automation, enterprise AI solutions, intelligent assistants, data agents, RAG systems, and multi-agent workflows.

More importantly, you will learn when AI is actually the right solution—and when it is better to use traditional software, rules, workflows, or human-in-the-loop approaches. You will also learn how to think about performance, scalability, reliability, security, and AI costs before deploying an application.

What You Will Learn

🤖 Build Autonomous AI Agents

• Understand how AI agents work
• Tool calling and function calling
• Agent reasoning and decision-making
• Memory and context
• Multi-agent architectures
• Agent orchestration

🏢 Enterprise-Level Agentic AI Frameworks

Hands-on experience with:

• LangChain
• LangGraph
• DeepAgents
• AutoGen

Understand the strengths, limitations, and appropriate use cases of different frameworks.

📚 Enterprise RAG

Build intelligent applications that can work with your organization's knowledge and documents.

• Retrieval-Augmented Generation
• Document processing
• Vector search
• Context management
• Enterprise knowledge assistants

🗄️ DBA & SQL Agents

Build agents that can intelligently interact with databases.

• SQL generation
• Database tools
• Query execution
• Data-aware agents
• Safe database interaction

📊 Analytics Agents

Learn how agents can work with structured data and perform analysis.

• Data analysis
• Python and pandas integration
• Automated insights
• Data visualization workflows
• Analytics automation

🎙️ Real-Time Voice AI

Understand how to build AI applications that can communicate through voice.

• Voice agents
• Speech-to-text
• Text-to-speech
• Real-time conversations
• Voice-based AI assistants

🔐 Guardrails & Security

Learn how to build safer and more reliable agentic applications.

• Agent guardrails
• Tool permissions
• Human-in-the-loop
• Security considerations
• Preventing uncontrolled agent actions

💰 AI Cost & Budgeting

Building an AI application is not just about making it work.

You will learn how to:

• Estimate token and API costs
• Select models based on requirements
• Balance quality, latency, and cost
• Optimize agent workflows
• Design cost-effective AI architectures
• Budget AI projects before deployment

🧠 AI Decision-Making

One of the most important skills is knowing when NOT to use AI.

You will learn how to evaluate a problem and decide whether the right solution is:

AI → Agent → Workflow → Rules → Traditional Software → Human

This helps avoid unnecessary complexity, excessive costs, and over-engineered solutions.

What You Will Build

Throughout the course, you will work on practical, real-world examples involving:

• Autonomous AI Agents
• Multi-Agent Systems
• Enterprise RAG
• DBA & SQL Agents
• Analytics Agents
• Voice AI
• Tool-using agents
• Production-oriented AI workflows

The focus is not just on understanding concepts—it is on building and thinking like an Agentic AI developer.

Who Is This For?

This course is suitable for:

• Developers who want to enter Agentic AI
• AI/ML engineers
• Software engineers
• Students with programming experience
• Professionals looking to build AI-powered applications
• Anyone who wants to move beyond basic prompt engineering

The goal is simple: Don't just chat with AI. Learn how to build AI systems that can actually do work.