This is a hands-on, code-first course focused on building real agentic AI systems in Python—not just prompt demos. You’ll design and implement a stateful, tool-using agent that can retrieve knowledge with RAG, expose capabilities through an MCP server, and coordinate multiple agents to plan, execute, and verify tasks.

Along the way, you’ll apply practical Python fundamentals, evaluation techniques, safety patterns, and production hardening practices. By the end, you’ll have a complete, extensible agentic application—and the architectural patterns to confidently build and ship your own.
 

AI Knowledge Hub Content Plan

Pillar Page 3: Model Context Protocol (MCP) Explained

Introduction

Model Context Protocol (MCP) is becoming a key standard for connecting AI models to enterprise systems, databases, tools, and applications. It provides a secure and consistent method for AI systems to access external resources and perform actions.

As organisations deploy AI agents at scale, MCP is emerging as an important technology for integration and interoperability.

What is Model Context Protocol (MCP)?

MCP is an open standard that enables AI systems to connect securely with external tools, applications, APIs, and data sources.

Why is MCP important?

MCP reduces integration complexity and provides a standard way for AI assistants and agents to interact with enterprise systems.

What can MCP connect to?

MCP can connect AI systems to databases, CRM platforms, document repositories, project management tools, cloud services, APIs, and business applications.

Is MCP only used for AI agents?

No. MCP can be used by AI assistants, copilots, chatbots, and agent-based systems whenever access to external resources is required.

Why is MCP often compared to USB-C?

Just as USB-C standardised device connectivity, MCP aims to standardise how AI systems connect to external tools and information sources.

What is Retrieval-Augmented Generation (RAG)?

Retrieval-Augmented Generation (RAG) is an AI architecture that combines Large Language Models with external knowledge sources. Instead of relying solely on information learned during training, a RAG system retrieves relevant information from documents, databases, or company knowledge repositories before generating a response.

RAG helps organisations build AI solutions that provide more accurate, up-to-date, and context-aware answers while reducing hallucinations. It is commonly used in enterprise chatbots, knowledge assistants, customer support systems, and internal search platforms.
 

What is Model Context Protocol (MCP)?

Model Context Protocol (MCP) is an open standard that allows AI models to securely connect to external tools, applications, databases, and business systems. It provides a consistent way for AI assistants and agents to access information and perform actions beyond their built-in capabilities.

MCP is often described as the "USB-C for AI" because it creates a standard interface between AI systems and external services. Organisations use MCP to integrate AI agents with CRM systems, document repositories, databases, project management platforms, and other enterprise applications.

What Is the Difference Between RAG and Fine-Tuning?

RAG and fine-tuning are two different approaches to improving AI performance.

RAG enhances responses by retrieving relevant information from external knowledge sources at runtime. This allows AI systems to access current information without retraining the model.

Fine-tuning modifies the model itself by training it on additional datasets to improve performance for specific tasks or domains.

Many enterprise AI solutions use RAG because it is faster, less expensive, easier to update, and better suited for knowledge that changes frequently.

What is RAG?

Retrieval-Augmented Generation (RAG) is an AI architecture that combines Large Language Models with external knowledge sources. A RAG system retrieves relevant information from documents, databases, or knowledge repositories before generating a response, improving accuracy and reducing hallucinations.