Training on Generative and Agentic AI for Decision-Makers

Module 1: Foundations of LLMs, Capabilities, Limitations, and Common Misconceptions

• Understanding what large language models are and how they work
• Exploring the capabilities and limitations of LLMs
• Debunking common myths and misconceptions about LLMs
• Understanding the technology behind LLMs (transformers, attention)
• Assessing the strategic implications of LLMs for business
• Case Study: Evaluating the capabilities of an LLM

Module 2: Identifying Viable Use Cases and Avoiding Hype-Driven Pitfalls

• Developing criteria for identifying viable AI use cases
• Differentiating between hype and genuine opportunities
• Evaluating the feasibility and impact of potential use cases
• Prioritizing use cases for implementation
• Avoiding common pitfalls in AI project selection
• Case Study: Identifying and prioritizing AI use cases

Module 3: The Evolution of Agentic Systems, Key Agent Types, and Orchestration Patterns

• Understanding the evolution from simple AI to agentic systems
• Exploring different types of AI agents (reactive, deliberative, hybrid)
• Understanding agent architectures and components
• Exploring orchestration patterns for multi-agent systems
• Evaluating the potential of agentic AI for business
• Case Study: Analyzing an agentic AI system

Module 4: Multi-Sector Examples Illustrating Where Agents Deliver Value

• Exploring agentic AI applications in customer service and support
• Examining agents in supply chain and logistics
• Understanding agents in finance and operations
• Exploring agents in healthcare and research
• Identifying cross-sector lessons and best practices
• Case Study: Analyzing an agentic AI application in a specific sector

Module 5: Designing a Use Case for Agentic AI

• Defining the problem statement and objectives
• Designing the agent architecture and workflow
• Identifying data and infrastructure requirements
• Planning for integration with existing systems
• Developing success metrics and evaluation criteria
• Case Study: Designing an agentic AI use case

Module 6: Building a Simple Agent on an Existing Platform

• Getting started with an agent development platform
• Configuring and customizing an AI agent
• Implementing agent workflows and logic
• Testing and refining agent performance
• Deploying the agent for a specific task
• Case Study: Building a simple agent on a platform

Module 7: Comparing Frameworks for Agent Development

• Evaluating different agent development frameworks
• Understanding the strengths and weaknesses of each framework
• Selecting the appropriate framework for your use case
• Comparing costs, capabilities, and scalability
• Making informed recommendations for agent development
• Case Study: Comparing agent development frameworks

Module 8: Producing Governance Artefacts for Internal or Client Review

• Developing governance documents for AI agents
• Creating policies and procedures for agent use
• Documenting risk assessments and mitigation strategies
• Preparing compliance documentation and audit trails
• Communicating governance frameworks to stakeholders
• Case Study: Developing governance artefacts for an AI agent

Module 9: Developing a Business Case for Investment, Including ROI Framing

• Building a compelling business case for AI investment
• Calculating ROI and total cost of ownership
• Identifying qualitative and quantitative benefits
• Presenting the business case to decision-makers
• Managing stakeholder expectations and buy-in
• Case Study: Developing a business case for AI investment

Module 10: Recommending Oversight and Governance Measures for Autonomous Workflows

• Understanding the governance needs of autonomous workflows
• Developing oversight mechanisms for AI agents
• Implementing monitoring and auditing processes
• Ensuring accountability and transparency in autonomous systems
• Making recommendations for continuous improvement
• Case Study: Recommending governance measures for an autonomous workflow