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 guidelines
• Ensuring consistency across development teams and AI tools
Level 2 | Can – Skills
Topics covered:
• Designing reusable modules of specialized knowledge
• Loading contextual information only when necessary
• Optimizing 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
• Expanding the capabilities of AI coding assistants
• Implementing secure integrations and governance controls
Level 4 | Acts – Agents
Topics covered:
• Understanding autonomous AI agents and their functionalities
• Enabling autonomous code reading, writing, testing, and revision
• 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:
• Distinguishing between AI assistants and autonomous agents
• Comparing inline code completion with agentic delegation
• Exploring how agentic workflows transform task structuring
• Identifying tasks suitable for agent delegation
• Best practices for collaborating with autonomous AI systems
Module 2 | Delegations That Work Without Babysitting
Topics covered:
• Crafting effective instructions for AI agents
• Providing adequate context and business requirements
• Defining execution constraints and boundaries
• Setting clear acceptance criteria and success metrics
• Minimizing human intervention while maintaining quality
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 applicable personalization mechanisms for various scenarios
• Efficiently managing context across tools and projects
• Creating consistent AI-assisted development environments
Module 4 | Skills and Subagents
Topics covered:
• Developing reusable Skills for common workflows and tasks
• Packaging specialized knowledge for repeated use
• 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
Topics covered:
• Grasping the principles of the Model Context Protocol (MCP)
• Linking AI tools to external systems and services
• Integrating browsers, databases, repositories, and documentation sources
• Developing custom MCP servers
• 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
• Collaboratively building and implementing solutions
• Testing and validating generated outputs
• Reviewing and finalizing deliverables with appropriate verification steps
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
• Enhancing 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 various industries
• Common AI adoption strategies: individual, team-based, and organizational-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
• Addressing current challenges within participants' development teams
• Identifying high-value use cases for immediate adoption
• Analyzing risks, blockers, and organizational considerations
• Creating an initial action plan for AI integration.
Requirements
Participants should possess professional development experience, be comfortable using the terminal, and have working knowledge of Git. Additionally, regular use of an AI coding tool or successful completion of the Foundations course is required.
Audience
Targeted at developers actively utilizing AI tools, technical leads overseeing team adoption strategies, and platform or DevOps engineers responsible for creating Skills and MCP servers.
Testimonials (2)
Using Claude Code in a more efficient way
Virgil Trif - Frequentis
Course - Claude Code: Agentic AI Development · 1-Day
"I learned the potential of the tool and gained sufficient skills to start using it for my work right away
