Course Outline
Introduction to Agentic AI and Bootcamp Overview
- Exploring the concept of agentic AI and the course structure
- Reviewing 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-focused conversational agent
- Leveraging structured prompts for reasoning and decision-making
- Development and testing within Jupyter Notebook
Project 2: Document Analysis and Summarization Agent
- Extracting and summarizing information from PDFs and text documents
- Utilizing LangChain for document ingestion and retrieval tasks
- Producing executive reports from both structured and unstructured data
Project 3: Tool-Using Agent for Workflow Automation
- Integrating external APIs to automate repetitive processes
- Handling multi-step procedures via agentic loops
- Constructing a compact automation assistant for actual business workflows
Project 4: Data Analysis and Insight Generation Agent
- Connecting agents to various data sources (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 multiple agents for efficient task delegation
- Architecting a controller–worker model
- Deploying and validating a prototype multi-agent system
Wrap-Up and Next Steps
- Project presentations and peer review sessions
- Exploration of optimization and scaling methodologies
- Providing resources for ongoing learning and experimentation
Requirements
- Adequate proficiency in Python programming
- Fundamental knowledge of AI or machine learning concepts
- Basic understanding of API usage and data processing pipelines
Target Audience
- Engineers and developers seeking to develop practical AI solutions
- Technical teams looking to improve rapid prototyping skills in agentic AI
- Professionals involved in AI integration and pilot project 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.
