Agentic AI: A Framework for Planning and Execution
1h 9mBeginner2026-03-18
Authors

Laurence Moroney
Course details
You've heard all about agents, and you've wondered if they will be useful for your business. This course shows you what they are, how you can use them, and where you can use them to drive business value through increased efficiency. Instructor Laurence Moroney digs deep into use-cases for agents, as well as patterns for building agentic systems, with a view to spurring inspirational scenarios within your business. Imagine what could happen if you improved everyone's productivity by just 5%. This course teaches you the tools to do just that, and potentially much more. Navigate the hype to understand how to get business value.
Learning objectives
Explore how agents can be used pragmatically.
Understand opportunistic use and how it may open up new scenarios
Understand how agents can help people in your company who worry about taking on technical debt decide how and what to use and trust.
Gain an understanding of what is going on in this space, and how it impacts your business.
Learning objectives
Explore how agents can be used pragmatically.
Understand opportunistic use and how it may open up new scenarios
Understand how agents can help people in your company who worry about taking on technical debt decide how and what to use and trust.
Gain an understanding of what is going on in this space, and how it impacts your business.
Skills covered
AI for Business FoundationsBusiness StrategyAI Productivity ToolsArtificial Intelligence for BusinessBusiness Analysis and StrategyLeadership and ManagementBusiness Software and ToolsOne-Off
Concepts
Introduction
- Introduction to agentic AI - Avoiding the hype
What Is an AI Agent
- What exactly is an agent
- How agents differ from AI ML models
- The concept of agency and autonomy
Business Applications
- How agents are used in industry
- When to use and not to use agents
- ROI considerations
- Implementation challenges and solutions
Building Blocks of Agents
- Core components - Perception, planning, action
- The role of LLMs in agents
- What are memory and context management
- Tool use and API integration
Architectural Considerations
- Single agents and multiagents
- Different approaches to agent design
Practical Considerations
- Security and safety considerations
- Resource requirements
- Integration with existing systems
- Monitoring and maintenance
Conclusion
- Emerging trends in agent technology