Course Outline
Introduction to Agentic AI and Bootcamp Structure
- Overview of agentic AI concepts and course organization
- Review of essential tools, frameworks, and dependencies
- Best practices for project configuration and team collaboration
Project 1: Intelligent Assistant with Prompt-Driven Reasoning
- Designing a conversational agent focused on specific tasks
- Leveraging structured prompts for reasoning and decision processes
- Implementation and testing within Jupyter Notebook
Project 2: Document Analysis and Summarization Agent
- Extracting and condensing information from PDFs and text documents
- Utilizing LangChain for document ingestion and retrieval tasks
- Producing executive summaries from mixed structured and unstructured content
Project 3: Tool-Using Agent for Workflow Automation
- Connecting external APIs to automate routine tasks
- Orchestrating multi-step processes via agentic loops
- Creating a lightweight automation assistant for actual business operations
Project 4: Data Analysis and Insight Generation Agent
- Linking agents to various data sources (CSV, SQL, or API)
- Conducting exploratory data analysis using Python and AI support
- Generating visual insights and performance dashboards
Project 5: Multi-Agent Collaboration and Orchestration
- Coordinating multiple agents for task distribution
- Architecting a controller–worker model
- Deploying and validating a prototype multi-agent system
Conclusion and Future Directions
- Project showcases and peer evaluations
- Exploration of optimization and scaling techniques
- Recommendations for ongoing learning and experimental work
Requirements
- Intermediate-level proficiency in Python programming
- Fundamental knowledge of AI or machine learning concepts
- Working familiarity with APIs and data processing pipelines
Target Audience
- Engineers and developers aiming to create applied AI solutions
- Technical teams seeking to accelerate prototyping capabilities in agentic AI
- Professionals engaged in AI integration and pilot program initiatives
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.
