AI Agents Course: LangChain, LangGraph & RAG

If you are serious about building AI Agents or Agentic AI, this course is worth saving. 🚀
Many AI Agent courses focus on building an agent that works in a demo. This course goes further: it covers the technologies and practices needed to build, control, evaluate, and move agentic systems toward production.

The course by Krish Naik follows a practical progression through:

• LangChain
• LangGraph
• RAG
• Vectorless RAG
• Deep Agents
• Guardrails
• LLM Evaluation
• LLM Gateways

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What You Will Learn​

The course is organized around the main stages of building an agentic AI system. 🧠

✴️ Build​

Start with the foundations of LLM applications and the frameworks used to build them.
You will work with technologies such as LangChain and LangGraph before moving into more advanced agentic patterns.

🔗 Connect​

Learn how agents can connect to external knowledge and capabilities through:

• RAG
• Vectorless RAG
• Tools
• Workflows
• External data sources

The goal is to move beyond a basic chatbot and build systems that can actually retrieve information and perform tasks.

🤖 Agentic​

Once the foundations are in place, the course moves toward more autonomous AI agents.
This includes concepts around Deep Agents and workflows where the model can make decisions and use tools as part of a larger process.

🛡️ Control​

More autonomy also means more things can go wrong.
The course covers Guardrails and safety techniques that can help control agent behavior and reduce unwanted outputs or actions.

📊 Evaluate​

Building an agent is only part of the job.
You also need to know whether it actually works well.
The course covers LLM evaluation concepts related to areas such as:

• Response quality
• Hallucinations
• Retrieval performance
• Agent behavior
• Overall system performance

Evaluation is especially important when an AI application moves from a small experiment to a system used by real users.

🚀 Production​

The final stage focuses on concerns that become important when running LLM applications at scale.
Topics include:

• Monitoring
• Routing
• LLM Gateways
• Cost control
• Production-oriented architecture

This is where the difference between a simple AI demo and a production-oriented AI system becomes much clearer.

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Why This Course Is Useful​

The biggest value of this course is the progression.
Instead of treating AI Agents as a single technology, it connects several pieces of the modern LLM stack:
LLMs → Frameworks → RAG → Tools → Agents → Guardrails → Evaluation → Production
That makes the course useful if you want to understand not only how to build an agent, but also what needs to happen before that agent can become part of a reliable application.

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Who Should Take It?​

This course is a good fit for developers who already have some programming experience and want to move into:

• Generative AI
• AI Agents
• Agentic AI
• LLM applications
• RAG systems
• LangChain and LangGraph
• LLM evaluation
• Production AI systems

If you are completely new to programming or LLMs, some sections may require additional study alongside the course.

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Watch the Course​

🎥 Generative AI and Agentic AI with LangChain and LangGraph

Save it if you are building your AI Agent roadmap. 🔖

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Frequently Asked Questions​

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Is this course only about building AI Agents?​

No. It covers a broader agentic AI stack, including LangChain, LangGraph, RAG, Vectorless RAG, Deep Agents, Guardrails, evaluation, and LLM gateways.

Does the course cover RAG?​

Yes. RAG is one of the topics covered, along with Vectorless RAG and related approaches to connecting AI systems with external knowledge.

Does it cover production topics?​

Yes. The course also covers production-oriented topics such as monitoring, routing, and cost control.

Who is this course for?​

It is mainly useful for developers and AI practitioners who want to build more complete Generative AI and Agentic AI applications rather than simple LLM demos.