Course Outline
The Four-Level Personalisation Stack
Level 1 | Knows – Rules and AGENTS.md
Key learning outcomes include: • Establishing project conventions and coding standards • Documenting system architecture and technical constraints • Creating tool-agnostic project guidance • Ensuring consistency across development teams and various AI tools
Level 2 | Can – Skills
Key learning outcomes include: • Developing reusable units of specialized knowledge • Dynamically loading contextual information only when necessary • Optimizing context size to enhance task performance • Constructing libraries of reusable workflows and expertise
Level 3 | Reaches – MCP
Key learning outcomes include: • Integrating AI tools with external systems and services • Accessing repositories, databases, and documentation sources • Extending the functional capabilities of AI coding assistants • Implementing secure integrations and governance controls
Level 4 | Acts – Agents
Key learning outcomes include: • Understanding the capabilities of autonomous AI agents • Autonomously reading, writing, testing, and refining code • Managing goal-driven workflows and delegated responsibilities • Implementing oversight and human review mechanisms for agentic systems
Day 1 | Delegation and Extending the Tools
Module 1 | From Assistant to Agent
Key learning outcomes include: • Distinguishing between AI assistants and autonomous agents • Comparing inline code completion with agentic delegation • Understanding how agentic workflows transform the structure of development tasks • Identifying tasks suitable for delegation to agents • Adopting best practices for collaborating with autonomous AI systems
Module 2 | Delegations That Work Without Babysitting & Loops
Key learning outcomes include: • Crafting effective instructions for AI agents • Providing sufficient context and business requirements • Defining execution constraints and boundaries • Establishing clear acceptance criteria and success metrics • Minimizing human intervention while maintaining quality standards • Building effective Loops
Module 3 | Personalisation Stack and What Applies Where
Key learning outcomes include: • Mastering the four-level personalisation stack • Utilizing Rules and AGENTS.md to define project conventions • Determining which personalisation mechanisms are appropriate for specific scenarios • Managing context efficiently across different tools and projects • Creating consistent AI-assisted development environments
Module 4 | Skills and Subagents
Key learning outcomes 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 specialized agents • Enhancing efficiency through modular AI workflows
Day 2 | Connecting Tools, Parallelism and Governance
Module 5 | MCP: Connect and Build
Key learning outcomes include: • 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
Key learning outcomes 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
• Setting up effective goals
Module 7 | Parallel Development
Key learning outcomes include: • 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
Key learning outcomes 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 learning outcomes 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
Participants should possess professional development experience, be proficient with command-line interfaces, and have a solid grasp of Git workflows. Regular use of an AI coding tool or prior completion of the Foundations course is recommended.
Target Audience
This course is tailored for developers actively using AI tools, technical leads overseeing team adoption, and platform or DevOps engineers focused on 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
