Building Your Own Agentic AI Application
Learning to build your own agentic AI application is more accessible than ever thanks to free tutorials, guides, courses, and example code available online. Agentic AI refers to autonomous software entities that perceive their environment, make decisions, and take actions to achieve goals. These AI agents are critical to many modern applications, from chatbots to complex multi-agent systems. Using these resources, you can develop skills to create AI agents that automate tasks efficiently, sometimes outperforming junior employees in speed and accuracy.
Understanding Model Context Protocol for AI Agents
The Model Context Protocol (MCP) is an open standard designed to connect AI models with external tools, APIs, and data sources. MCP servers span categories such as browser automation, cloud platforms, and code execution, allowing AI agents to access broader information and capabilities. For example, integrating MCP servers enables AI agents to search the internet for stock prices, analyze markets, and even execute trades in real time, which has led to millions in profits for some users. Exploring MCP clients like Python frameworks and VSCode extensions helps you build versatile agentic applications.
Top GitHub Repositories for Learning AI Agents
There are at least ten key GitHub repositories that provide structured paths to understanding AI agents and large language models (LLMs).
These include: – Tutorials with 11 lessons focused on building your first AI agents. – Comprehensive projects using Jupyter Notebook with code and output demonstrations. – A 6-week Agentic AI Engineering course with hands-on coding and deployment. – Collections of system prompts and models from popular AI tools like Cursor and Replit Agent. These repositories offer practical examples and step-by – step guidance, ideal for beginners and intermediate learners aiming to deepen their understanding of agentic AI.
Advantages of Agentic AI in Automation
Agentic AI applications are increasingly used to automate repetitive tasks, improving efficiency and reducing workforce needs. Companies report that agentic AI can outperform junior-level employees in certain functions, providing faster and more reliable outputs. For instance, AI agents connected to MCP servers can gather real-time data, perform analysis, and take autonomous actions—capabilities beyond typical static AI models. This automation potential is a key reason why agentic AI is dominating the AI space today.
Combining Agents
Combining AI Agents with Retrieval-Augmented Generation. Advanced AI projects combine agentic AI with retrieval-augmented generation (RAG) and MCP servers to enhance the abilities of large language models. By doing so, AI agents can access external knowledge dynamically, overcoming some limitations of static LLMs. For example, projects utilizing models from OpenAI, Anthropic, and Gemini demonstrate how these integrations improve accuracy and context awareness. This approach not only broadens AI capabilities but also opens new opportunities for creating intelligent, interactive applications.
Challenges with Large Language Models and the Future
Despite excitement around artificial general intelligence, large language models have limitations, and some companies manipulate benchmarks to overstate model performance. The future of AI appears to be heading toward systems that combine agentic AI with protocols like MCP to extend functionality and reliability. These hybrid approaches enable AI to perform complex workflows, handle real-world data, and maintain context over time, which are essential for practical, impactful applications.
Practical Steps to Start Building Agentic AI
To get started building your own agentic AI application, follow these best practices: – Explore curated GitHub repositories offering free tutorials, code, and projects. – Study MCP server and client lists to understand available external tools and data sources. – Experiment with Jupyter Notebook projects to see real-time AI agent outputs. – Enroll in structured courses like the 6-week Agentic AI Engineering program for guided learning. – Combine agentic AI with retrieval-augmented generation and cutting-edge models for advanced capabilities. By following these steps, you can accelerate your learning curve and begin creating AI applications that automate workflows and solve real problems.
About the Author and Resources
Abid Ali Awan is a certified data scientist professional with a passion for machine learning and AI content creation. Holding a Master’s degree in technology management and a bachelor’s in telecommunication engineering, Abid focuses on building AI products that address mental health challenges. For those interested, free resources like ‘The Great Big Natural Language Processing Primer’ and ‘The Complete Collection of Data Science Cheat Sheets’ are available through KDnuggets newsletters to support your AI learning journey.
