Learn to build intelligent AI agents that break down complex tasks into manageable steps, leverage external tools, and maintain conversation context across interactions. This practical tutorial guides you through creating a research agent that gathers information from multiple sources and synthesises findings into coherent insights. You'll explore the core concepts of agentic AI, including task decomposition, tool integration, and memory management. By the end, you'll have hands-on experience building agents that can reason about problems, decide which tools to use, and learn from previous interactions. Perfect for developers looking to move beyond simple chatbots and build truly autonomous systems that can tackle real-world problems independently.

Lessons

  1. Introduction to Agentic AI — Understanding agents versus traditional LLMs and core concepts (+50 XP)
  2. Agent Architecture & Design Patterns — Building the foundation: loops, states, and decision-making (+75 XP)
  3. Implementing Tool Use & Function Calling — Enabling agents to call external APIs, databases, and services (+100 XP)
  4. Building Memory Systems — Short-term context and long-term knowledge retention strategies (+100 XP)
  5. Building Your Research Agent — Putting it together: a practical multi-source research system (+125 XP)
  6. Debugging & Optimising Agent Behaviour — Testing, monitoring, and improving agent performance (+75 XP)
  7. Production Patterns & Best Practices — Scaling agents safely, error handling, and reliability (+100 XP)