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AI Agents Fundamentals
Rating: 4.5 out of 5(92 ratings)
2,388 students

AI Agents Fundamentals

Your complete, hands-on exploration of AI Agents
Created byMumshad Mannambeth, KodeKloud Training
Last updated 9/2026
English
English [Auto],

What you'll learn

  • Build with LangChain: Use the LangChain framework to create practical, AI-driven applications, starting from your very first API call.
  • Master Prompt Engineering: Apply effective prompt engineering techniques directly within LangChain to precisely control and steer AI behavior.
  • Implement RAG & Vector Databases: Build and utilize a vector database to create a semantic search engine and implement the RAG architecture.
  • Design Stateful Workflows: Use LangGraph to construct complex, stateful AI workflows that can perform multi-step tasks and integrate with external tools.

Course content

2 sections • 27 lectures • 58m total length

  • Introduction to AI Agents0:40

    Master ai agents fundamentals by exploring prompt engineering, context windows, and embeddings, then uncover rag vector, dbms agents, long chain, lane graph, lang chain, cloud, gemini, and MCP prompt engineering.

  • How LLMs work in real time4:17

    Learn how transformer-based large language models use context windows and tokens to process prompts, and how to feed internal data via the conversation context window while balancing latency.

  • Embeddings & Vector Representations0:59

    Embeddings convert text into 1536-number vectors to capture meaning and semantic similarity. Similar concepts have close vectors, enabling retrieval by meaning rather than exact words.

  • How LangChain works4:17

    Lang Chain provides an abstraction layer to build AI agents with memory, tools, and multi-provider support, simplifying integration of vector databases, embeddings, and external systems.

  • Practice Labs – Your First AI API Call4:43

    Verify the Python environment and API keys to make your first OpenAI chat API calls, initialize the client, parse responses, and understand token costs, plus LangChain for multi-provider workflows.

  • Course Setup: Accessing the labs0:23
  • Lab: Your First AI API Call0:01
  • Practice Labs – LangChain3:04

    Explore LangChain's universal interface, verify your environment, and compare boilerplate with LangChain across OpenAI, Gemini, and Zeiss Grok; learn prompts, parsers, and chain composition for multi-provider AI pipelines.

  • Lab: LangChain0:01
  • Prompt Engineering Techniques3:23

    Explore prompt engineering techniques to improve AI agent responses by crafting specific prompts and role definitions, covering zero shot, one shot, few shot, and chain of thought prompting.

  • Practice Labs - Master Prompt Engineering3:22

    Master prompt engineering with structured prompting techniques—zero shot, one shot, few shot, and chain of thought—through hands-on labs that compare methods and reveal the most effective approach for AI responses.

  • Lab: Prompt Engineering with LangChain0:01

Requirements

  • There are no prerequisites required for this course.

Description

Unlock the power of next-generation AI Agents with this hands-on, practical learning experience. This comprehensive course guides you step-by-step through the essentials of building, deploying, and managing sophisticated AI-driven applications. You'll move beyond simple chatbots to create autonomous agents that can reason, plan, and interact with their environment using industry-leading tools and frameworks.

Your journey begins with the fundamentals. You'll start by making your very first AI API call, demystifying the connection between your code and a large language model. From there, you'll progressively deepen your understanding through practical labs on LangChain, the premier framework for agent development. Master powerful prompt engineering techniques to precisely control your agent's behavior. Then, discover how to give your agent a "memory" by harnessing vector databases, culminating in a real-world project: building a semantic search engine for technical documentation.

With the foundation set, you'll explore advanced architectures. Dive deep into the RAG (Retrieval-Augmented Generation) approach, learning how to connect your agents to external knowledge sources to provide accurate, up-to-date answers. You'll then be introduced to LangGraph, a powerful tool for building complex, stateful AI workflows. This allows your agents to perform multi-step tasks, make decisions, and loop until a goal is achieved. The course concludes with advanced concepts, where you'll extend LangGraph capabilities by integrating external tools and APIs.

Whether you're new to AI or a developer looking to integrate agentic capabilities, this course provides the skills and confidence to create your own intelligent workflows. By the end, you won't just understand AI agents; you'll be building and deploying them from scratch, ready to tackle complex, real-world problems.

Who this course is for:

  • Developers who want to learn how to build and integrate modern AI agent capabilities into their new or existing applications.
  • AI Enthusiasts & Hobbyists who understand the basic concepts of AI and LLMs but want a practical, hands-on path to building real-world projects.
  • Data Scientists & Analysts looking to expand their skill set beyond data analysis into the realm of automated, agent-based systems and workflows.
  • Tech Professionals & Engineers who are new to the AI agent ecosystem and want a clear, step-by-step introduction to foundational tools like LangChain, vector databases, and LangGraph.

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