Course Outline
The Four-Level Personalisation Stack
Level 1 | Knows – Rules and AGENTS.md
Topics covered:
• Establishing project conventions and coding standards
• Documenting architecture and technical constraints
• Developing tool-neutral project guidance
• Ensuring consistency across development teams and AI tools
Level 2 | Can – Skills
Topics covered:
• Creating reusable units of specialized knowledge
• Loading contextual information only when necessary
• Reducing context size while enhancing task performance
• Building libraries of reusable workflows and expertise
Level 3 | Reaches – MCP
Topics covered:
• Linking AI tools to external systems and services
• Accessing repositories, databases, and documentation sources
• Extending the capabilities of AI coding assistants
• Implementing secure integrations and governance controls
Level 4 | Acts – Agents
Topics covered:
• Understanding autonomous AI agents and their capabilities
• Autonomously reading, writing, testing, and revising code
• Managing goal-driven workflows and delegated tasks
• Establishing oversight and human review mechanisms for agentic systems
Day 1 | Delegation and Extending the Tools
Module 1 | From Assistant to Agent
Topics covered:
• Understanding the distinction between AI assistants and autonomous agents
• Comparing inline code completion with agentic delegation
• Exploring how agentic workflows reshape development task structures
• Identifying tasks suitable for delegation to agents
• Best practices for collaborating with autonomous AI systems
Module 2 | Delegations That Work Without Babysitting & Loops
Topics covered:
• Writing effective instructions for AI agents
• Providing sufficient context and business requirements
• Defining constraints and boundaries for execution
• Establishing clear acceptance criteria and success measures
• Minimizing human intervention while maintaining quality
• Strategies for building Loops
Module 3 | Personalisation Stack and What Applies Where
Topics covered:
• Understanding the four-level personalisation stack
• Using Rules and AGENTS.md to define project conventions
• Determining which personalisation mechanisms apply in different scenarios
• Managing context efficiently across tools and projects
• Creating consistent AI-assisted development environments
Module 4 | Skills and Subagents
Topics covered:
• Creating reusable Skills for common workflows and tasks
• Packaging specialist knowledge for repeated use
• Understanding the role of subagents and isolated contexts
• Delegating bounded tasks to specialized agents
• Improving efficiency through modular AI workflows
Day 2 | Connecting Tools, Parallelism and Governance
Module 5 | MCP: Connect and Build
Topics covered:
• Understanding the principles of the Model Context Protocol (MCP)
• Connecting AI tools to external systems and services
• Integrating browsers, databases, repositories, and documentation sources
• Building a custom MCP server
• Managing access control and security considerations
Module 6 | The Disciplined Agentic Workflow
Topics covered:
• Establishing a repeatable AI-assisted development process
• Brainstorming and planning with AI agents
• Building and implementing solutions collaboratively
• Testing and validating generated outputs
• Reviewing and finalizing deliverables with appropriate verification steps
• Setting up goals
Module 7 | Parallel Development
Topics covered:
• Running multiple AI agents simultaneously
• Working with isolated branches and Git worktrees
• Coordinating development activities across parallel workflows
• Merging and validating outputs from multiple agents
• Improving productivity through parallel execution strategies
Module 8 | Risks, Review and Governance
Topics covered:
• 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
Topics covered:
• How organizations 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 organization-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 organizational considerations
• Creation of an initial action plan for AI integration.
Requirements
Participants should have professional development experience, comfort with using the terminal, and working knowledge of Git. Additionally, candidates must regularly use an AI coding tool or have completed the Foundations course.
Audience
This course is ideal for developers already utilizing AI tools, technical leads overseeing team adoption, and platform or DevOps engineers responsible for creating 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
