Course Outline
The Four-Level Personalisation Stack
Level 1 | Knows – Rules and AGENTS.md
Key topics include:
• Establishing project conventions and coding standards
• Documenting architectural decisions and technical constraints
• Creating tool-agnostic project guidance
• Ensuring consistency across development teams and various AI tools
Level 2 | Can – Skills
Key topics include:
• Developing reusable units of specialist knowledge
• Loading contextual data on-demand
• Optimising context size while enhancing task performance
• Building libraries of reusable workflows and expert insights
Level 3 | Reaches – MCP
Key topics include:
• Linking AI tools to external systems and services
• Accessing repositories, databases, and documentation sources
• Expanding the functional scope of AI coding assistants
• Implementing secure integrations and governance controls
Level 4 | Acts – Agents
Key topics include:
• Grasping the capabilities of autonomous AI agents
• Autonomously reading, writing, testing, and refining code
• Managing goal-oriented workflows and delegated responsibilities
• Implementing oversight and human review mechanisms for agentic systems
Day 1 | Delegation and Tool Extension
Module 1 | From Assistant to Agent
Key topics include:
• Distinguishing between AI assistants and autonomous agents
• Comparing inline code completion with agentic delegation
• Understanding how agentic workflows reshape development task structuring
• Identifying tasks suitable for agent delegation
• Best practices for collaborating with autonomous AI systems
Module 2 | Delegations That Work Without Babysitting & Loops
Key topics include:
• Crafting effective instructions for AI agents
• Supplying adequate context and business requirements
• Defining execution constraints and boundaries
• Setting clear acceptance criteria and success metrics
• Minimising human intervention while preserving quality
• Techniques for building Loops
Module 3 | Personalisation Stack and What Applies Where
Key topics include:
• Navigating the four-level personalisation stack
• Using Rules and AGENTS.md to define project conventions
• Selecting appropriate personalisation mechanisms for specific scenarios
• Efficiently managing context across tools and projects
• Fostering consistent AI-assisted development environments
Module 4 | Skills and Subagents
Key topics include:
• Creating reusable Skills for common workflows and tasks
• Packaging specialist knowledge for repeated application
• Understanding the role of subagents and isolated contexts
• Delegating bounded tasks to specialised agents
• Enhancing efficiency through modular AI workflows
Day 2 | Tool Connectivity, Parallelism and Governance
Module 5 | MCP: Connect and Build
Key topics include:
• Understanding the principles of the Model Context Protocol (MCP)
• Linking AI tools to external systems and services
• Integrating browsers, databases, repositories, and documentation sources
• Developing a custom MCP server
• Managing access control and security considerations
Module 6 | The Disciplined Agentic Workflow
Key topics include:
• Establishing a repeatable AI-assisted development process
• Brainstorming and planning with AI agents
• Collaboratively building and implementing solutions
• Testing and validating generated outputs
• Reviewing and finalising deliverables with appropriate verification steps
• Strategies for setting up goals
Module 7 | Parallel Development
Key topics include:
• Executing multiple AI agents simultaneously
• Utilising isolated branches and Git worktrees
• Coordinating development activities across parallel workflows
• Merging and validating outputs from multiple agents
• Boosting productivity through parallel execution strategies
Module 8 | Risks, Review and Governance
Key topics include:
• Evaluating and vetting external Skills and MCP servers
• Understanding security and governance risks
• Managing permissions and access rights
• Protecting sensitive data and intellectual property
• Establishing review processes and quality assurance practices
Module 9 | AI Adoption in Software Development: Use Cases and Next Steps
Key topics include:
• How organisations are integrating AI into the Software Development Lifecycle (SDLC)
• Real-world use cases and implementation examples from different industries
• Common AI adoption approaches: individual adoption, team-based adoption and organisation-wide enablement
• Typical use cases across the SDLC:
• Requirements gathering and documentation
• Code generation and prototyping
• Testing and quality assurance
• Code review and refactoring
• Documentation and knowledge management
• DevOps and incident management
• Governance models, policies and security considerations
• Measuring productivity and ROI of AI-assisted development
• Building an internal AI adoption roadmap
• Defining practical next steps for participants and their teams
Interactive Discussion Workshop
• Current challenges within the participants' development teams
• Identification of high-value use cases for immediate adoption
• Risks, blockers and organisational considerations
• Creation of an initial action plan for AI integration.
Requirements
Candidates should possess professional development experience, proficiency with the terminal, and practical Git knowledge. Regular usage of an AI coding tool or successful completion of the Foundations course is also required.
Audience
The course is tailored for developers already integrating AI into their workflows, technical leads overseeing team adoption, and platform or DevOps engineers responsible for developing Skills and MCP servers.
Testimonials (2)
Learning how to prompt Claude and use it to digest all of the data I have available.
Mike Hartleroad - Furniture Row
Course - Claude AI for Data Analysis and Business Intelligence
how to engage with the Office environment and set up repetitive tasks
