Course Outline
Introduction to Agentic AI and Course Structure
- Exploring agentic AI concepts and the intensive’s framework
- Overview of necessary tools, frameworks, and dependencies
- Best practices for project configuration and collaborative work
Project 1: Intelligent Assistant Utilizing Prompt-Based Reasoning
- Designing a task-oriented conversational agent
- Leveraging structured prompts to drive reasoning and decisions
- Construction and testing within a Jupyter Notebook environment
Project 2: Document Analysis and Summarization Agent
- Extracting and summarizing information from PDFs and text documents
- Utilizing LangChain for document ingestion and retrieval processes
- Producing executive summaries from both structured and unstructured data
Project 3: Tool-Integrating Agent for Workflow Automation
- Connecting external APIs to automate routine tasks
- Orchestrating multi-step processes using agentic loops
- Creating a lightweight automation assistant for practical business workflows
Project 4: Data Analysis and Insight Generation Agent
- Linking agents to diverse data sources such as CSV files, SQL databases, or APIs
- Conducting exploratory data analysis with assistance from Python and AI
- Generating visual insights and performance dashboards
Project 5: Multi-Agent Collaboration and Orchestration
- Coordinating multiple agents for effective task delegation
- Architecting a controller–worker system design
- Deploying and validating a prototype multi-agent system
Conclusion and Future Directions
- Project presentations and peer review sessions
- Review of strategies for optimization and system scaling
- Access to resources for ongoing learning and experimentation
Requirements
- Intermediate proficiency in Python programming
- Foundational understanding of AI or machine learning concepts
- Experience with APIs and data processing workflows
Target Audience
- Engineers and developers aiming to create applied AI solutions
- Technical teams requiring rapid prototyping capabilities in agentic AI
- Professionals involved 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.
