Course Outline
Introduction to Agentic AI and Bootcamp Structure
- Exploring the fundamentals of agentic AI and the course organization
- Overview of necessary tools, frameworks, and dependencies
- Recommended practices for setting up projects and collaborating effectively
Project 1: Intelligent Assistant Focused on Prompt-Based Reasoning
- Designing a conversational agent for specific tasks
- Leveraging structured prompts to drive reasoning and decision-making
- Implementation and testing within Jupyter Notebook
Project 2: Agent for Document Analysis and Summarization
- Extracting and condensing information from PDFs and text documents
- Utilizing LangChain for document ingestion and retrieval processes
- Producing executive reports from both structured and unstructured data
Project 3: Agent for Workflow Automation Using Tools
- Integrating external APIs to automate routine tasks
- Handling multi-step processes via agentic loops
- Developing a compact automation assistant for actual business operations
Project 4: Agent for Data Analysis and Insight Generation
- Linking agents to various data sources such as CSV, SQL, or APIs
- Conducting exploratory data analysis using Python with AI support
- Generating visual insights and performance dashboards
Project 5: Multi-Agent Collaboration and Orchestration
- Coordinating several agents to delegate tasks effectively
- Designing a controller–worker system architecture
- Deploying and evaluating a prototype multi-agent system
Conclusion and Future Directions
- Project presentations and exchange of peer feedback
- Exploring strategies for optimization and scaling
- Resources for ongoing learning and further experimentation
Requirements
- Intermediate-level proficiency in Python programming
- Fundamental understanding of AI or machine learning concepts
- Knowledge of APIs and data processing workflows
Target Audience
- Engineers and developers aiming to create applied AI solutions
- Technical teams seeking to enhance their rapid prototyping capabilities in agentic AI
- Professionals engaged in AI integration and the development of pilot programs
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.
