In a previous hands-on articles, we built agents using local LLMs, and another article hands-on with AI-Integrated Development Environment using Claude Code. This article continues that journey, Here the focus is different: 👉 How Claude Code becomes an Agent Platform — not just a coding assistant. In this article, we will teach Claude how to work with us. We will progressively build: Skills, Agents, MCP tools, Plugin integrations amd by the end, Claude will act as: an About-Me agent, a Books price agent, and an externally powered MCP agent

In Agentic AI Hands-On — Part 1, we built our first AI Agent and explored how a well-designed prompt can drive real-world actions — browsing websites, calling APIs, or performing calculations through tools. We implemented the agent using the basic architecture Thought → Action → Observation → Reasoning → Answer Where the agent relied on OpenAI LLMs hosted over cloud and accessed through a python SDK. While powerful, this setup requires:

We often hear that times are changing. In the AI era, change is no longer gradual — it is accelerated. Today, AI agents assist us in writing, thinking, researching, and even making personal decisions. Many of us now do something unimaginable a few years ago: We even confirm what our doctors say… with AI. AI explains reports. AI answers late-night doubts. AI reassures us when anxiety grows. It is that second opinion!

In my previous article, I explored the journey from AI → GenAI → Agentic AI and also emphasized why being hands-on still matters, even when AI can generate code in seconds. The role of developers is clearly evolving—from pure builders to reviewers, orchestrators, and system designers. Today, AI Agents are everywhere. We hear about systems that: Generate code, review, test and deploy it Perform tasks autonomously Integrate seamlessly with applications But there’s a gap.

The world of technology is evolving at lightning speed. Every week, new AI models, frameworks, and services make headlines. With the hype around LLMs, Agentic AI, MCPs, and A2A systems, it’s easy to feel like we’re always playing catch-up. But here’s the truth: without strong fundamentals, chasing the “next big thing” is like building a skyscraper on sand. A strong foundation ensures stability. That’s why, before diving deep into Generative AI, it’s critical to revisit the path that got us here.