AI Agents Learned Through Practical Experience (Agents) Part 1

This covers how to connect local, API, and MCP tools to AI models and build agents applicable to real-world tasks. You will learn various tool integration methods and orchestration concepts while developing the ability to design single, parallel, and chain-based execution topologies. You will understand and be able to implement agent architectures that automate complex database control and integration with external systems.

This is a course that beginners can follow along with in a fun and easy way. Beyond simple chatbots, AI Agents that automate industry-specific business workflows. This course quickly covers the basic structure and core technologies of AI Agents (LangChain, LangGraph, RAG) in 1.5 hours, and is an introductory course where you build practical skills by directly creating mini agents that work with actual code. After completing the course, you'll understand the necessity of industry-specific data preprocessing and scalable design, and be prepared for advanced concepts covered in deeper courses.

[Source Code Provided] No coding knowledge required. Build it instantly in your browser using Google's latest tool (IDX) without any installation! Stop studying coding syntax! This is an ultra-fast, hands-on course where you'll build an RAG chatbot that perfectly understands internal company documents through AI conversation (Vibe Coding) in the Google IDX environment and deploy it live to the web.

Go beyond a simple system prompt to build an AI character chatbot that remembers users and whose relationship with them evolves, using persona design and Structured Outputs. In this 30-minute hands-on session, you’ll learn how to safely call the OpenAI API based on the provided starter project, process emotions, trust levels, and memories in a verifiable format using JSON Schema and Zod, and use an AI coding agent within defined constraints.

We examine AI security incidents and the role of red teams, then set up an LLM practice environment in our own AWS account. From capturing the initial response to checking usage and cleaning up resources, we record everything in the Mission 0 checklist. The prompt injection diagnosis exercise continues in Part 4.

We design and implement AI agent systems that go beyond simple chatbots to perform tasks autonomously. From Sense-Think-Act loop-based architectures to LangGraph cyclic structures, RAGAS and LangSmith-based quantitative evaluation, and operational guardrails, you will complete your enterprise-grade agent engineering capabilities through hands-on practice of the entire process.

Explore how LLM applications are shifting the development paradigm. Understand why LLMs are becoming a sustainable core technology through comparisons with existing technologies, and learn about the essential skills and mindset shifts required for developers. Gain insights from expert instructors to grasp the essence and future direction of LLM application development.

LLM: A pro quickly delivers core content, from essentials to practical tips!

Develop various features using Spring WebFlux. This course will teach you WebFlux's essential concepts and practical implementation.

Review the AI service’s settings, including keys, access scope, call limits, execution permissions, guardrails, and logging. Compare the controls before and after while keeping other conditions unchanged, and review output handling to create the Mission 4-2 settings checklist and diagnostic record.

If you want to create your own AI service without coding, this course is the perfect solution. Using the Dify no-code platform, you can create high-quality AI apps with just your ideas. Instead of complex theory, it's structured around hands-on projects, allowing both AI beginners and working professionals to develop practical skills.

Test the agent’s authorization boundaries for tool calls and RAG search and document access. Using the actual model and training mock tools and documents, observe the approval gates and search permission filters, then complete the Mission 5·6 diagnostic worksheet.

This is a hands-on tutorial on how to integrate with IntelliJ and develop quickly using the Model Context Protocol (MCP).

We explore how to create an AI judge by combining knowledge graphs, reasoning systems, and various agent frameworks such as CrewAI, LangChain, and AutoGen, which enable the implementation of LLM-based multi-agent technology.

Build your own Copilot Agent to automate the repetitive reading, organizing, summarizing, and drafting tasks that occur every day in a Microsoft 365 work environment. Learn how to create a practical work automation agent that delivers immediate ROI—not an ambitious AI project—and use it as your digital work partner.

Beyond a chatbot that simply generates answers, you will directly implement an autonomous AI Agent that makes its own judgments, calls tools, and performs multi-step tasks. You will learn through step-by-step practice, covering everything from OpenAI Agents SDK's Tool Calling, Handoff, Guardrail, Session, and Tracing to connecting external tools and databases using MCP, as well as LangChain and Deep Agents' Planning, Subagents, Filesystem, and Memory. The ultimate goal is to equip you with the ability to design and implement tool-integrated AI Agents that can be applied to real-world tasks.

Learn the easiest way to handle AI with Spring AI. Let's learn Spring AI by building a RAG chatbot.

"How is a 1-trillion parameter model trained?" Discover the scaling strategy of Kimi K2.5, revealed directly on stage by Moonshot AI at GTC 2026, in Korean on Inflearn. 2x token efficiency · 1 million token context · 1,000 agent swarm The next step for open-source LLMs is here. * This lecture is released for free through an official collaboration between Moonshot AI, the creator of Kimi, and Inflearn.

As an ambassador for the Cloud Native Computing Foundation (CNCF), I have given talks at KubeCon on topics such as Kubernetes security and policy, the scheduler, and Gateway API. I also serve as an ambassador for the Agentic AI Foundation (AAIF), where I present findings from hands-on research and measurements of AI agents, LLMs, MCP, and AGENTS.md in Kubernetes environments. This course includes Korean-language recordings of talks I have given in English at international events such as KubeCon, Open Source Summit, MCP Dev Summit, and AGNTCon + MCPCon, and I add each recording as it becomes available.

Learn the entire process of running the latest Gemma 4 model directly on your MacBook without the burden of paid API costs. You will master performance maximization techniques using Apple Silicon's Metal API and optimal parameter settings for different VRAM capacities, while gaining the ability to build production-level local AI infrastructure based on FastAPI.