Course Outline
Introduction to Agentic AI and Bootcamp Structure
- Exploring agentic AI concepts and the bootcamp’s learning path
- Reviewing essential tools, frameworks, and dependencies
- Establishing best practices for project setup and team collaboration
Project 1: Intelligent Assistant Utilizing Prompt-Oriented Reasoning
- Designing a conversational agent focused on task execution
- Leveraging structured prompts for logical reasoning and decision-making
- Developing and testing the agent within a Jupyter Notebook
Project 2: Agent for Document Analysis and Summarization
- Extracting and condensing data from PDFs and text documents
- Utilizing LangChain for document ingestion and retrieval processes
- Generating executive reports from both structured and unstructured data
Project 3: Tool-Enabled Agent for Workflow Automation
- Integrating external APIs to automate repetitive business tasks
- Orchestrating multi-step processes using agentic loops
- Constructing an automation assistant tailored for real-world business workflows
Project 4: Agent for Data Analysis and Insight Generation
- Connecting agents to various data sources, including CSV, SQL, or APIs
- Conducting exploratory data analysis with the aid of Python and AI
- Developing visual insights and performance dashboards
Project 5: Multi-Agent Collaboration and Orchestration
- Coordinating multiple agents to delegate and execute complex tasks
- Architecting a controller–worker system design
- Deploying and validating a prototype multi-agent system
Conclusion and Future Pathways
- Project presentations and constructive peer feedback
- Strategies for system optimization and scalable growth
- Recommended resources for ongoing learning and experimentation
Requirements
- Intermediate level proficiency in Python programming
- Foundational knowledge of AI or machine learning concepts
- Experience with APIs and data processing workflows
Target Audience
- Engineers and developers focused on creating applied AI solutions
- Technical teams seeking to accelerate prototyping capabilities in agentic AI
- Professionals involved in AI integration and pilot program 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.
