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-neutral project guidance
• Ensuring consistency across development teams and AI integrations
Level 2 | Can – Skills
Key topics include:
• Developing reusable units of specialist knowledge
• Loading contextual data only when necessary
• Optimising context size to enhance task performance
• Building libraries of reusable workflows and expertise
Level 3 | Reaches – MCP
Key topics include:
• Linking AI tools to external systems and services
• Accessing code repositories, databases, and documentation sources
• Expanding the capabilities of AI coding assistants
• Implementing secure integrations and governance controls
Level 4 | Acts – Agents
Key topics include:
• Understanding the capabilities of autonomous AI agents
• Autonomously reading, writing, testing, and refining code
• Managing goal-oriented 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
Key topics include:
• Distinguishing between AI assistants and autonomous agents
• Comparing inline code completion with agentic delegation
• Examining how agentic workflows restructure development tasks
• 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
• Providing adequate context and business requirements
• Defining execution constraints and boundaries
• Setting clear acceptance criteria and success metrics
• Minimising human intervention while preserving quality
• Constructing Loops
Module 3 | Personalisation Stack and What Applies Where
Key topics include:
• Grasping the four-level personalisation stack
• Utilising Rules and AGENTS.md to define project conventions
• Determining appropriate personalisation mechanisms for various scenarios
• Efficiently managing context across tools and projects
• Creating 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 | Connecting Tools, Parallelism and Governance
Module 5 | MCP: Connect and Build
Key topics 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
• 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
• Setting up goals
Module 7 | Parallel Development
Key topics include:
• Executing multiple AI agents simultaneously
• Working with 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
Participants should possess professional development experience, proficiency with the terminal, and a solid grasp of Git. Regular usage of an AI coding tool or prior completion of the Foundations course is required.
Audience
Developers actively using AI tools, technical leads overseeing team adoption, and platform or DevOps engineers responsible for building Skills and MCP servers.
Testimonials (2)
Considerations about privacy and policy
Angelo Stefano Soglia - Parkino srl
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