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:

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.

Integrated Development Environments (IDEs) have played a crucial role in shaping the way software is developed. Engineering Practices such as Agile development, eXtreme Programming, Test-Driven Development (TDD), became practical largely because IDEs provided a visual and integrated workflow for writing and managing code. In the early days, developers interacting heavily on command-line. Formatting code, navigating files, searching references, and refactoring logic were mostly manual tasks. Over time, IDEs transformed this experience.

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.

Unlock the power of Generative AI by learning to interact with large language models programmatically—and take it further with fine-tuning for customized responses. If you’re a software maker feeling the fear of missing out (FOMO) on the Gen AI revolution, you’re not alone. The way we build and interact with software is evolving rapidly. “Google it” or “check Stack Overflow” is no longer the go-to approach — AI-first, conversational tools are becoming the new standard across development, design, and decision-making.