Course Outline
The Four-Level Personalization Stack
Level 1 | Knows – Rules and AGENTS.md
Topics covered:
• Establishing project conventions and coding standards
• Documenting system architecture and technical constraints
• Developing tool-agnostic project guidance
• Ensuring consistency across development teams and AI utilities for government applications
Level 2 | Can – Skills
Topics covered:
• Creating reusable units of specialized knowledge
• Loading contextual data only when necessary
• Reducing context volume while enhancing task performance
• Building libraries of reusable workflows and expertise
Level 3 | Reaches – MCP
Topics covered:
• Connecting 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 relevant for government use cases
Level 4 | Acts – Agents
Topics covered:
• Understanding autonomous AI agents and their operational capabilities
• Enabling autonomous code reading, writing, testing, and revision
• Managing goal-oriented workflows and delegated tasks
• Establishing oversight and human review mechanisms for agentic systems to ensure accountability
Day 1 | Delegation and Extending Tool Capabilities
Module 1 | From Assistant to Agent
Topics covered:
• Distinguishing between AI assistants and autonomous agents
• Comparing inline code completion with agentic delegation
• Analyzing how agentic workflows restructure development tasks
• Identifying tasks suitable for delegation to agents
• Establishing best practices for collaboration with autonomous AI systems
Module 2 | Effective Delegation Without Supervision
Topics covered:
• Drafting 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 output quality for government operations
Module 3 | Personalization Stack Application
Topics covered:
• Understanding the four-level personalization stack
• Using Rules and AGENTS.md to define project conventions
• Determining appropriate personalization mechanisms for specific 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 specialized 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 for government data
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
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 for government projects
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 compliant with federal standards
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 sectors
• Common AI adoption approaches: individual, team-based, and organizational 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 for government IT infrastructure
• 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
• Assessment of risks, blockers, and organizational considerations
• Creation of an initial action plan for AI integration to support government mission objectives
Requirements
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
