Agentic AI: Tool Integration, MCP, and Agent Frameworks

Learn to build reliable AI agents with Agentic AI frameworks

As businesses increasingly adopt AI agents to automate complex workflows, understanding how to build, deploy, and manage these systems has become an essential AI engineering skill.

On this three-week Agentic AI course, you’ll explore how to move beyond building simple agents from scratch and develop production-ready AI systems using industry-standard tools, protocols, and frameworks.

Explore AI tool integration and Model Context Protocol

This course will help you build custom tools for AI agents, including web search, database access, API integrations, calculators, and file input and output systems.

You’ll also gain hands-on experience with Model Context Protocol (MCP), learning how to build MCP servers and clients from scratch. By understanding how MCP works, you’ll develop the skills needed to create flexible, scalable AI systems that can interact with multiple tools and services.

Compare leading Agentic AI frameworks

With a growing ecosystem of frameworks available, it can be challenging to determine which approach is best for your use case.

You’ll build and compare the same AI agent using LangGraph, OpenAI Agents SDK, and CrewAI, exploring the strengths and limitations of each framework.

This knowledge will help you make informed decisions about when to use each framework.

Learn to develop secure and reliable AI systems

Throughout the course, you’ll explore the practical considerations for deploying production-ready AI systems, including authentication, error handling, retry logic, and robust agent-tool pipelines.

This course is for technology enthusiasts, software developers, data professionals, product managers, and business leaders who want to understand agentic AI.

Learners should understand basic LLM concepts, prompt engineering, Python basics, APIs, and agent design patterns such as ReAct or reflection. Completion of Course 1 is recommended.

Learners need a computer (Windows, macOS, or Linux) with at least 8 GB RAM and a stable internet connection. Python 3.10 or later should be installed, along with a code editor such as VS Code. No paid software or specialised hardware is required.

This course is for technology enthusiasts, software developers, data professionals, product managers, and business leaders who want to understand agentic AI.

Learners should understand basic LLM concepts, prompt engineering, Python basics, APIs, and agent design patterns such as ReAct or reflection. Completion of Course 1 is recommended.

• Explain how tools, structured outputs, and function calling help AI agents interact with external systems.
• Apply function calling workflows by defining tool schemas, routing model requests, executing Python functions, and returning tool results.
• Develop custom agent tools and MCP-based interactions for controlled access to resources, databases, and external services.
• Compare agent framework approaches using LangGraph, OpenAI Agents SDK, and CrewAI for research-agent workflows.
• Evaluate secure and resilient tool workflows using authentication, permission scoping, retries, backoff, fallback behaviour, and production-readiness checks.