Course Taxonomy: Data & AI

Building Agentic Apps with the OpenAI SDK

Posted on October 28, 2025 by -

Part 1: What Are AI Agents

  • What is an AI agent?
  • How agents differ from chatbots and traditional automation (e.g., RPA)
  • Intro to agentic workflows and autonomy
  • Overview of different AI Agentic frameworks and tools. 
  • Group Discussion: “Where could agents make your work easier or more powerful?”
  • Live Q&A

Part 2 – Anatomy of an AI Agent

  • Core components: memory, planning, tool use, feedback
  • Best practices for Agent design
  • Multi-agent systems and Model Context Protocol
  • Hands-On Exercise: “Map an agent architecture to a sample workflow”(e.g., lead follow-up, report generation, task triage)
  • Live Q&A

Part 3 – Real-World Use Cases + Tools

  • Agents in ops, PM, research, sales, customer service
  • Tool demo: no-code agent builder
  • Tool stack examples using OpenAI and others
  • Group Exercise: “Given a use case, choose the best tool and justify your pick”
  • Discussion: “What blockers would you face deploying this in your org?”
  • Live Q&A

Part 4 – Strategy, Piloting & Ethics

  • Prototyping your first agent
  • Governance, trust, and AI feedback loops
  • Measuring ROI, value, and readiness
  • Hands-On Walkthrough: “Build and run a simple agent using a no code AI Agent tool”
  • Observe and analyze output
  • Live Q&A + Next Steps
  • Summary of takeaways

Intro to the Future of Work using AI Agents

Posted on October 28, 2025 by -

Part 1: What Are AI Agents 

  • What is an AI agent? 
  • How agents differ from chatbots and traditional automation (e.g., RPA) 
  • Intro to agentic workflows and autonomy 
  • Overview of different AI Agentic frameworks and tools.  
  • Group Discussion: “Where could agents make your work easier or more powerful?” 
  • Live Q&A 

Part 2 – Anatomy of an AI Agent 

  • Core components: memory, planning, tool use, feedback 
  • Best practices for Agent design 
  • Multi-agent systems and Model Context Protocol 
  • Hands-On Exercise: “Map an agent architecture to a sample workflow”(e.g., lead follow-up, report generation, task triage) 
  • Live Q&A 

 

Part 3 – Real-World Use Cases + Tools 

  • Agents in ops, PM, research, sales, customer service 
  • Tool demo: no-code agent builder 
  • Tool stack examples using OpenAI and others 
  • Group Exercise: “Given a use case, choose the best tool and justify your pick” 
  • Discussion: “What blockers would you face deploying this in your org?” 
  • Live Q&A 

 

Part 4 – Strategy, Piloting & Ethics 

  • Prototyping your first agent 
  • Governance, trust, and AI feedback loops 
  • Measuring ROI, value, and readiness 
  • Hands-On Walkthrough: “Build and run a simple agent using a no code AI Agent tool” 
  • Observe and analyze output 
  • Live Q&A + Next Steps 
  • Summary of takeaways 

AI Native Foundations

Posted on September 8, 2025 by -

Introduction to AI-Native Foundations

    • Overview of course objectives and outcomes
    • Introduction to the EDGE™ Imperative

Part 1: Grasp the EDGE™ Imperative

    • Explore Exponential, Disruptive, Generative, and Emergent forces transforming work
    • Discuss the impact of these forces on various industries

Part 2: Understanding AI and Related Technologies

    • Simplified explanations of AI, ML, GenAI, LLMs, RAG, and intelligent agents
    • Master safe and effective AI prompting with proven techniques
    • Real-world examples and applications

Part 3: Applying AI-Native Success Factors

    • Introduction to the 7 AI-Native Success Factors
    • Case studies and practical applications to drive value from day one

Part 4: Workflow Improvement and Transformational Thinking

    • Redesign one of your personal workflows using AI
    • Business Brief: Identifying high-impact opportunities
    • Strategies for transformational thinking in AI adoption

Part 5: Roadmap to AI-Native

    • Develop a strategic approach to becoming AI-Native
    • Tools and strategies for implementation

Part 6: The AI-Native Pitch

    • Design a personal 30-60-90 day AI plan
    • Pitch AI use cases with confidence
    • Workshop: Crafting and delivering effective AI pitches

Conclusion and Next Steps

    • Recap of key learnings
    • Strategies for continued AI fluency and confidence
    • Q&A and feedback session

SAFe® Achieving Responsible AI

Posted on March 18, 2025 by -

Self-paced, on-demand eLearning (1 hour)

The eLearning module comprehensively introduces Responsible AI, covering foundational concepts and their alignment with SAFe practices. The eLearning includes several pre-work assignments intended to fully prepare students for the facilitated session — including researching current RAI practices in your organization, reviewing example policies, and ensuring access to a generative AI tool for practical exercises.

Activity 1 – Identifying Stakeholders (~30 minutes)

As future change agents and advocates for RAI, one of the first and most important tasks is to think about the people in an organization who would need to be involved in implementing RAI, what their responsibilities would be, as well as their top concerns. Learners will leave this activity with an actionable stakeholder map for future initiatives.

Activity 2 – Evaluating RAI Policies (~25 minutes)

Get hands-on experience using GenAI applications such as SAFe CoPilot and ChatGPT to apply critical thinking to an example set of policy elements in an RAI plan, identifying improvements needed for organizational fit.

Activity 3 – Communicating the Need for RAI (~30 minutes)

Become empowered to effectively communicate the need for Responsible AI (RAI), the goals of an RAI initiative, and the alignment of an RAI initiative with organizational objectives through a concise “elevator pitch” activity.

Activity 4 – Writing an RAI Epic Hypothesis Statement (~35 minutes)

Practice identifying an actionable element of an RAI implementation that requires a significant effort by one or more Value Streams, and scoping that effort using a SAFe Epic hypothesis statement.

AI for Leaders & Managers

Posted on November 6, 2024 by -

Part 1: Introduction to Leadership Needs Emerging from AI

  • Overview of AI technologies relevant to management
  • Potential impacts on team structures and workflows

 

Part 2: Strategizing AI Integration

  • Identifying AI opportunities in team operations
  • Aligning AI projects with organizational objectives
  • Workshop Activity: Creating an AI integration roadmap for your team

 

Part 3: Leadership in the Age of AI

  • Techniques for leading through technological change
  • Building and leading cross-functional AI integration teams
  • Interactive Session: Role-playing AI-driven change scenarios

 

Part 4: Optimizing Team Performance with AI

  • Leveraging AI for decision support and performance enhancement
  • Case Studies: Successful team transformations through AI

 

Part 5: Ethical Leadership and AI Governance

  • Navigating ethical issues in AI application
  • Developing an AI governance framework for your team

 

Part 6: Workshop on AI and Team Dynamics

  • Managing changes in roles and skills due to AI adoption
  • Fostering a culture of innovation and continuous learning

 

Part 7: Metrics and Measurement

  • Defining and tracking success metrics for AI initiatives
  • Tools for monitoring AI impact on team effectiveness

 

Part 8: Q&A, Discussion, and Action Planning

  • Addressing specific challenges faced by participants
  • Developing personal action plans based on workshop insights
  • Wrap-up and feedback session

AI for Executives

Posted on November 6, 2024 by -

Part 1: Introduction to Generative AI

  • What is Generative AI?
  • Current and emerging technologies
  • Overview of Impacts
  • Industry use cases and success stories

 

Part 2: Strategic Planning with AI

  • Aligning AI with business goals and strategies
  • Identifying AI opportunities within your business processes
  • Understanding the most immediately accessible value and initiatives
  • Workshopping Alignment: Mapping potential AI applications to business units

 

Part 3: Leadership in AI Implementation

  • Leading change and fostering an AI-ready culture
  • Understanding and leading the necessary technology stakeholders
  • Building in-house AI capabilities vs. partnering
  • Case Study Analysis: Real-world examples of successful executive leadership on AI projects

 

Part 4: Risk Management, Ethics, and Legal Liability Concerns

  • Navigating the ethical implications of AI
  • Managing data privacy and security risks
  • Developing a compliance framework for AI applications

 

Part 5: ROI and Metrics

  • How to think about AI-driven value
  • Setting up success metrics for AI projects
  • Tools for tracking and analyzing AI project performance
  • ROI expectations and reality checks

 

Part 6: Q&A and Wrap-Up

  • Open discussion to address specific concerns and scenarios from participants
  • Summary of key takeaways
  • Next steps and resources for further learning

Advanced Splunk Boot Camp

Posted on August 27, 2024 by -

Advanced Splunk Boot Camp

Part 1: Advanced Data Ingestion

  • Advanced Indexing Concepts
  • Handling High Volume Data
  • Data Parsing and Transformation
  • Exercise: Advanced Data Parsing Techniques

Part 2: Advanced Search Processing Language (SPL)

  • Advanced Search Commands
  • Data Models and Pivots
  • Creating and Using Macros
  • Exercise: Writing Advanced SPL Queries
  • Custom Commands and Scripts
  • Exercise: Developing Custom Commands
  • Transaction Searches and Anomalies
  • Exercise: Complex Searches and Data Correlation

Part 3: Performance Optimization

  • Search Performance Tuning
  • Resource Management
  • Index and Search Head Performance Optimization
  • Exercise: Optimizing Search Performance
  • Monitoring Console and Usage Dashboards
  • Exercise: Using Monitoring Console for Optimization

Part 4: Security and Monitoring

  • Role-Based Access Control (RBAC)
  • Data Integrity and Confidentiality
  • Auditing and Monitoring User Activity
  • Exercise: Implementing Security Best Practices
  • Incident Detection and Response
  • Exercise: Building Incident Response Dashboards

Part 5: Advanced Dashboard and Visualization

  • Advanced Dashboarding Techniques
  • Custom Visualization Options
  • Integrating with External Systems
  • Exercise: Creating Advanced Dashboards
  • Real-time Dashboards and Alerts
  • Exercise: Building Real-time Monitoring Dashboards

Part 6: Splunk Machine Learning Toolkit



  • Introduction to the Splunk Machine Learning Toolkit
  • Building Machine Learning Models in Splunk
  • Using Pre-built Machine Learning Algorithms
  • Exercise: Implementing Machine Learning Use Cases
  • Anomaly Detection and Predictive Analytics
  • Exercise: Building and Applying Predictive Models
  • Monitoring and Tuning Machine Learning Models

Introduction to AI & Machine Learning eLearning

Posted on June 18, 2024 by -

Part 1: Introduction to AI

  • Overview: What is AI and its significance?
  • History: Brief evolution of AI and its modern applications.
  • Ethics: Considerations on AI ethics, bias, privacy, and societal impacts.

Part 2: AI Concepts

  • AGI, ANI, ASI: Explanation of different AI levels and their implications.

Part 3: Machine Learning Basics

  • Goals: Objectives of ML-like prediction and pattern recognition.
  • Types:
    • Supervised Learning: Using labeled data for tasks like classification.
    • Unsupervised Learning: Identifying patterns in unlabeled data.
    • Semi-supervised Learning: Leveraging both labeled and unlabeled data.
  • Reinforcement Learning: Learning from interactions with an environment.

Part 4: Assessment

AI for Business Analysis

Posted on May 9, 2024 by -

Part 1: Understanding AI’s Role in Business Analysis

Part 2: Using AI to Jumpstart a Project

  • Applying prompt engineering techniques to plan and refine a product

Part 3: Organizing AI-Created Content

  • Transforming AI outputs and transforming them into coherent, valuable resources

Part 4: Crafting User Stories with AI

Part 5: AI and Stakeholder Interviews

  • Training simulated interviews by taking on
  • personas and responding to questions

Part 6: Potential Pitfalls and Social Risks

  • Detecting “hallucinations” and critically evaluating and validating AI results

Part 7: Requirements Analysis and Solution Design

  • Using AI to create many valuable BA artifacts such as process models and ERDs

Part 8: AI-Assisted UI Design

  • Transforming AI outputs into visual representations to produce UI prototypes

Part 9: Writing Tests with AI

  • Creating test scenarios and evaluating results to catch errors or gaps in coverage

Part 10: AI for Complete, Consistent, & Coherent Analysis

  • Strategies for responsible creation of AI-created artifacts under human supervision

Part 11: Creative Applications of Generative AI

  • Using generative AI for writing, education, and presentation design.

Part 12: Implementing AI-Driven Business Analysis

  • Responsibly leveraging AI's potential business analysis under human supervision

AI for Software Testing

Posted on May 9, 2024 by -

Part 1: Introducing Generative AI for Software Testing

Part 2: Let’s Test with AI

  • Use AI agents to generate and run tests

Part 3: Modelling for Testing

  • Apply different ways to structure a problem and organize the testing process

Part 4: Test Planning with AI

  • Use AI to help create an overall test strategy, using a Test Strategy Canvas and, Testing Quadrants

Part 5: Testing Single Functions

  • Learn how AI can assist with equivalence partitioning, boundary value analysis, state and preconditions when defining tests

Part 6: Evaluate Tests

  • Identifying missing and redundant tests as well as the level of test coverage

Part 7: Activities and Processes

  • Use AI to generate use cases in several forms (traditional, Given-When-Then, and graphical) and generate detailed test cases

Part 8: Planning the End Game

  • Create AI-generated test plans for UAT, alpha, beta, and usability testing

Part 9: Stories and Scenarios

  • Use AI to present a user story in terms of a set of scenarios that need to pass

Part 10: Automation

  • Use AI to generate automated test cases

Part 11: Quality Attributes & Non-functional Requirements

Part 12: Evaluating AI Readiness

  • Ethical considerations and emerging trends