Course Outline
Introduction to Agentic AI and Programme Overview
- Understanding agentic AI and the bootcamp structure
- Review of tools, frameworks, and dependencies
- Best practices for project setup and collaboration
Project 1: Intelligent Assistant with Prompt-Oriented Reasoning
- Designing a task-based conversational agent
- Utilising structured prompts for reasoning and decision-making
- Building and testing in Jupyter Notebook
Project 2: Document Analysis and Summarization Agent
- Extracting and summarising data from PDFs and text files
- Using LangChain for document ingestion and retrieval
- Generating executive reports from structured and unstructured content
Project 3: Tool-Using Agent for Workflow Automation
- Integrating external APIs and automating repetitive tasks
- Managing multi-step processes through agentic loops
- Building a small automation assistant for real business workflows
Project 4: Data Analysis and Insight Generation Agent
- Connecting agents to data sources (CSV, SQL, or API)
- Performing exploratory data analysis with Python and AI assistance
- Creating visual insights and performance dashboards
Project 5: Multi-Agent Collaboration and Orchestration
- Coordinating multiple agents for task delegation
- Designing a controller–worker architecture
- Deploying and testing a prototype multi-agent system
Conclusion and Next Steps
- Project presentations and peer feedback
- Discussion of optimisation and scaling strategies
- Resources for continued learning and experimentation
Requirements
- Intermediate-level proficiency in Python programming
- Fundamental understanding of AI or machine learning concepts
- Experience with APIs and data processing workflows
Target Audience
- Engineers and developers aiming to create applied AI solutions
- Technical teams looking to acquire rapid prototyping skills in agentic AI
- Professionals engaged in AI integration and pilot programme development
Testimonials (1)
First most valuable: Practical data analysis tasks using AI without copy-pasting - but through my own application of what I learned. Second most valuable: definition of terms that everyone uses (skills, agents, MCP, API). Now that I understand them, I feel much more confident in trying AI Third: your input on how AI works, what it can do, what it can't (I will remember your 80/20 rule forever). Fourth: Having everything you taught us in written and accessible form.
