Course Outline
Introduction to Agentic AI and Course Structure
- Overview of agentic AI concepts and the bootcamp's learning pathway.
- Review of essential tools, frameworks, and dependencies.
- Best practices for setting up projects and fostering effective collaboration.
Project 1: Intelligent Assistant with Prompt-Oriented Reasoning
- Designing a conversational agent focused on specific tasks.
- Leveraging structured prompts to enhance reasoning and decision-making capabilities.
- Developing and testing the agent within a Jupyter Notebook environment.
Project 2: Document Analysis and Summarization Agent
- Extracting and summarizing key data from PDFs and text files.
- Utilizing LangChain for efficient document ingestion and retrieval.
- Generating executive reports from both structured and unstructured content.
Project 3: Tool-Using Agent for Workflow Automation
- Integrating external APIs to automate repetitive operational tasks.
- Managing complex, multi-step processes through agentic loops.
- Building a compact automation assistant tailored for real business workflows.
Project 4: Data Analysis and Insight Generation Agent
- Connecting agents to diverse data sources such as CSV files, SQL databases, or APIs.
- Conducting exploratory data analysis with the assistance of Python and AI tools.
- Creating visual insights and performance dashboards for better decision-making.
Project 5: Multi-Agent Collaboration and Orchestration
- Coordinating multiple agents for efficient task delegation.
- Designing a robust controller–worker architecture.
- Deploying and testing a prototype multi-agent system.
Conclusion and Future Pathways
- Project presentations and constructive peer feedback.
- Discussion on optimization and scaling strategies for production environments.
- Recommended resources for continued learning and further experimentation.
Requirements
- Intermediate proficiency in Python programming
- Basic understanding of AI or machine learning principles
- Familiarity with APIs and data processing workflows
Target Audience
- Engineers and developers aiming to build applied AI projects
- Technical teams seeking rapid prototyping skills in agentic AI
- Practitioners 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.
