Programme Content
Module 1: Foundations of Agentic Systems This module equips learners with the technical foundation needed to build and deploy intelligent agents. Starting with a Python refresher focused on concurrency and API interactions, the module moves into the key building blocks of AI and Large Language Models, including tokenisation, context windows, and parameter tuning. It concludes with practical approaches to prompt engineering, covering persona design, structured outputs, and control over model behaviour. Topics Covered: Python Refresher for Agentic AI AsyncIO and API Interactions Introduction to AI and LLMs Transformer Architecture and Tokenisation Temperature, Top-P, and Sampling Prompt Engineering: Zero/Few-shot, System Prompts, Structured Output Module 2: Agentic AI Fundamentals This module introduces the core workflows and infrastructure behind agentic systems. Learners will understand how agents perceive, plan, and act in cycles, use tools and memory eectively, and route tasks based on user intent. This module also introduces the Model Context Protocol (MCP) for exposing local data to agents and explores two leading orchestration frameworks (LangGraph and CrewAI) for designing flexible, modular agent pipelines. Topics Covered: Agent Workflows and Architecture Tool Use and Function Calling Router Patterns for Task Delegation Memory and Knowledge Retrieval (RAG) Vector Databases and GraphRAG Model Context Protocol (MCP) Orchestration with LangGraph and CrewAI Module 3: Advanced Agentic Systems This module focuses on building more intelligent and adaptive agents. Learners will implement step-by-step reasoning (CoT), interleaved tool use (ReAct), and reflection mechanisms that help agents learn from past actions. Reinforcement learning techniques and prompt optimisation using DSPy are introduced, along with methods for coordinating multiple agents through voting, debate, and role handos. Topics Covered: Planning, Reasoning, and Decision-Making (CoT, ReAct, Plan-and-Solve) Self-Reflection and Trajectory Optimisation Dataset Preparation for Fine-Tuning Reinforcement Learning and Reward Training Best-of-N Sampling and Feedback Loops DSPy and Auto-Prompting Multi-Agent Communication and Coordination Module 4: AI Agents in the Real World The final module prepares learners to build deployable, safe, and trustworthy agentic systems. Key practices in monitoring, human-in-the-loop workflows, and prompt security are covered, alongside strategies for scaling and exposing agents via modern deployment stacks. Learners will explore trade-os in autonomy, ethical design, and best practices for testing and maintaining agent behaviour in production.
