Course Outline
The Four-Tier Personalization Framework for Government Applications
Tier 1 | Knowledge Foundation – Rules and AGENTS.md
Curriculum Elements:
• Establishment of project standards and coding guidelines
• Documentation of architectural patterns and technical limitations
• Development of tool-agnostic operational directives
• Ensuring operational consistency across development teams and automated systems
Tier 2 | Capability – Skills
Curriculum Elements:
• Creation of modularized units of specialized technical knowledge
• Dynamic loading of contextual data based on operational requirements
• Optimization of context scope to enhance task execution efficiency
• Development of reusable workflow libraries for standardized processes
Tier 3 | Integration – MCP
Curriculum Elements:
• Linking artificial intelligence tools with external government systems and services
• Access to authorized repositories, databases, and documentation sources
• Expansion of AI assistant capabilities for operational support
• Implementation of secure integrations and governance controls for government use
Tier 4 | Action – Agents
Curriculum Elements:
• Analysis of autonomous AI agent capabilities and boundaries
• Autonomous execution of code reading, writing, testing, and revision
• Management of goal-oriented workflows and delegated operational tasks
• Establishment of oversight and human review mechanisms for agentic systems for government compliance
Day 1 | Delegation Protocols and Tool Extension
Module 1 | Transition from Assistant to Agent
Curriculum Elements:
• Differentiation between AI assistants and autonomous agents
• Comparison of inline code completion versus agentic task delegation
• Structural impacts of agentic workflows on development task management
• Identification of operational tasks suitable for agent delegation
• Best practices for collaboration with autonomous AI systems in government contexts
Module 2 | Effective Delegation and Automation Loops
Curriculum Elements:
• Formulation of precise instructions for AI agents
• Provision of comprehensive context and business requirements
• Definition of execution constraints and operational boundaries
• Establishment of clear acceptance criteria and performance metrics
• Reduction of manual intervention while ensuring quality standards
• Construction of automated feedback loops
Module 3 | Personalization Framework Application
Curriculum Elements:
• Understanding the four-tier personalization framework
• Utilization of Rules and AGENTS.md to define project standards
• Determination of applicable personalization mechanisms for specific scenarios
• Efficient context management across multiple tools and projects
• Creation of consistent AI-assisted development environments for government projects
Module 4 | Skills and Subagents
Curriculum Elements:
• Development of reusable Skills for standard workflows and tasks
• Packaging specialized knowledge for repeated operational use
• Understanding the role of subagents and isolated operational contexts
• Delegation of bounded tasks to specialized agents
• Enhancement of efficiency through modular AI workflow strategies
Day 2 | System Integration, Parallel Processing, and Governance
Module 5 | MCP: Connection and Development
Curriculum Elements:
• Analysis of Model Context Protocol (MCP) principles
• Connection of AI tools to external systems and services
• Integration of browsers, databases, repositories, and documentation sources
• Construction of custom MCP servers
• Management of access control and security protocols for government compliance
Module 6 | Structured Agentic Workflow
Curriculum Elements:
• Establishment of a repeatable AI-assisted development process
• Planning and ideation with AI agents
• Collaborative construction and implementation of solutions
• Testing and validation of generated outputs
• Review and finalization of deliverables with verification steps
• Configuration of operational goals
Module 7 | Parallel Development
Curriculum Elements:
• Simultaneous execution of multiple AI agents
• Utilization of isolated branches and Git worktrees
• Coordination of development activities across parallel workflows
• Integration and validation of outputs from multiple agents
• Productivity enhancement through parallel execution strategies
Module 8 | Risk Management, Review, and Governance
Curriculum Elements:
• Evaluation and vetting of external Skills and MCP servers
• Assessment of security and governance risks
• Management of permissions and access rights
• Protection of sensitive data and intellectual property
• Establishment of review processes and quality assurance practices
Module 9 | AI Adoption in Software Development: Use Cases and Strategic Steps
Curriculum Elements:
• Organizational integration of AI into the Software Development Lifecycle (SDLC)
• Real-world use cases and implementation examples across various sectors
• Common AI adoption models: individual, team-based, 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
• Measurement of productivity and return on investment for AI-assisted development
• Development of an internal AI adoption roadmap
• Definition of practical next steps for participants and their teams
Interactive Discussion Workshop
• Current challenges within participants’ development teams
• Identification of high-value use cases for immediate implementation
• Analysis of risks, barriers, and organizational considerations
• Development of an initial action plan for AI integration for government operations
Requirements
Professional development experience, proficiency with command-line interfaces, and comprehensive Git knowledge. Regular utilization of an AI coding tool or completion of the Foundations course is required.
Target Audience
Developers actively using AI tools, technical leads responsible for team adoption strategies, and platform or DevOps engineers developing Skills and MCP servers for government infrastructure.
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