Course Outline
Introduction to Agentic AI and Bootcamp Structure
- Overview of agentic AI and the bootcamp’s organizational structure
- Review of essential tools, frameworks, and dependencies
- Best practices for setting up projects and fostering collaboration
Project 1: Intelligent Assistant with Prompt-Oriented Reasoning
- Designing a task-specific conversational agent
- Leveraging structured prompts for reasoning and decision-making processes
- Construction and testing within a Jupyter Notebook environment
Project 2: Document Analysis and Summarization Agent
- Extracting key information and summarizing data from PDFs and text files
- Utilizing LangChain for document ingestion and retrieval tasks
- Generating executive reports from both structured and unstructured content
Project 3: Tool-Using Agent for Workflow Automation
- Integrating external APIs to automate repetitive operational tasks
- Managing multi-step processes via agentic loops
- Creating a small automation assistant tailored for real business workflows
Project 4: Data Analysis and Insight Generation Agent
- Connecting agents to diverse data sources (CSV, SQL, or APIs)
- Conducting exploratory data analysis with the support of Python and AI
- Developing visual insights and performance dashboards
Project 5: Multi-Agent Collaboration and Orchestration
- Coordinating multiple agents for efficient task delegation
- Designing a controller–worker architecture model
- Deploying and testing a prototype multi-agent system
Wrap-Up and Future Directions
- Project presentations and peer review feedback
- Discussion of strategies for optimization and system scaling
- Recommended resources for ongoing learning and experimentation
Requirements
- Intermediate skill level in Python programming
- Foundational grasp of AI or machine learning concepts
- Working knowledge of APIs and data processing workflows
Target Audience
- Engineers and developers interested in creating applied AI solutions
- Technical teams aiming 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.
