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GEN5138 Mastering AI-Powered Software Development

$199.00
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The Executive Diagnostic and Governance Toolkit

Mastering AI-Powered Software Development

Score your own function red, amber or green, find out which part is weakest, and walk into the next budget round able to defend what you want to fix. Built for leaders reviewing decide whether to adopt AI coding agents for core development workflows and justify the investment.

$199 one-time
30-day money-back guarantee Verified against latest insights, updated access provided within 24h

Each order is checked and updated against the latest insights before delivery. That is why access takes up to 24 hours rather than being instant.

What you walk out with
A scored, ranked picture of your own function, and a defensible answer to what to fix first.
1 You stop guessing where you stand.
You finish with a score, not an opinion: every part of your function rated red, amber or green, with the weakest ranked first. Evidence: a Quick Scan for the shape of it, then seven domain assessments of 30 scored questions each, 210 in all, rolled into one scorecard, plus a maturity radar and a current-versus-target gap analysis.
2 You can defend the decision.
You walk into the budget round with the gap named, the owner named and done defined, instead of a case built on instinct. Evidence: project charter, scope statement, RACI, requirements traceability and work breakdown structure, pre-filled in your domain's language.
3 The work actually moves.
The month after the decision is already built, so nothing stalls waiting for someone to design a form. Evidence: more than 60 project templates across all five PMBOK process groups, plus runbooks, SOPs, a KPI framework, audit checklists and a risk matrix. 55 to 65 files in total.
4 You use it the day it lands.
No blank templates to interpret. Every workbook opens with what it is, who uses it, when, how, a 1 to 5 scoring guide, what good looks like, and a worked example you delete and type over.
The Quick Scan is one sitting. You will know your weakest area before the day is out.
Nothing in it is generic project management: the build rejects any file that could belong to another course. Updated after you enrol, so it reflects where the work stands now. The 144-chapter course is included behind it, for the parts you want to go deeper on.
You're responsible for software quality, but AI agents are now writing, testing, and merging code without your direct oversight.

The situation this is built for

Your teams are adopting tools that generate code, create pull requests, and run test suites with minimal human input. You didn't initiate this shift, but you're accountable for the outcomes. Technical debt is compounding in unpredictable ways. Code reviews take longer because the logic is harder to trace. Architects are bypassed as agents scaffold entire services overnight. You're expected to maintain stability, security, and velocity — but the development model has changed underneath you. There is no playbook for leading in this environment, and no clear way to assess whether these tools are helping or harming your long-term goals.

Who this is for

Engineering leaders with direct responsibility for software delivery, architecture governance, and team productivity in organizations adopting AI coding agents. They manage mid-to-large engineering teams and are accountable for system reliability, velocity, and technical strategy.

Who this is not for

Individual contributors exploring AI tools for personal productivity, startups building AI-native products, or technical founders without legacy systems or governance responsibilities.

What you walk away with

  • Map the current state of AI adoption across your development lifecycle
  • Evaluate the real impact of AI agents on code quality and team dynamics
  • Define governance boundaries for AI-generated code in production systems
  • Align architectural oversight with autonomous coding workflows
  • Build a phased integration strategy that preserves engineering ownership

How this maps to your situation

  • You're responsible for systems but don't control the tools creating them
  • Your team's output has increased, but so has unpredictability
  • Architectural drift is accelerating beyond governance reach
  • You need to act before AI becomes the default developer

Before vs. after

Before
Overwhelmed by uncontrolled AI adoption, unclear ownership, and rising technical debt in agent-generated code.
After
Equipped with a clear assessment framework, governance model, and integration strategy for AI coding agents.

What's included with your purchase

  • 12 modules with 12 chapters each (144 chapters)
  • Downloadable templates and worked examples for every module
  • Hand-built implementation playbook delivered alongside course access
  • 30-day money-back guarantee

Delivery and format

  • Course and learning environment access provisioned within 24 hours of purchase
  • Hand-built implementation playbook delivered alongside course access

Format: Text-based modules and chapters in the Art of Service learning environment, plus downloadable templates and worked examples for every chapter, plus the hand-built implementation playbook delivered alongside course access.

Time investment: Approximately 3 hours per module, designed to be completed alongside regular responsibilities over 8 to 12 weeks.

If nothing changes
Without deliberate oversight, AI agents will become the default developers — making irreversible architectural decisions, accumulating untraceable technical debt, and eroding team expertise — leaving engineering leaders accountable for systems they no longer understand or control.

How this compares to the alternatives

Unlike vendor-specific training or generic AI primers, this course focuses exclusively on the leadership, governance, and operational challenges of integrating AI coding agents into established engineering organizations — with actionable frameworks rather than theoretical overviews.

Also included: the full course, for when you want the reasoning behind a finding (12 modules, 144 chapters)

Depth reference. The diagnostic and the templates stand on their own; this is what to read when you want the reasoning behind a finding.

Module 1. Understanding the Shift to Agent-Driven Development
Establish a shared mental model of how AI coding agents change the software lifecycle and redefine engineering ownership.
12 chapters in this module
  1. Identifying where AI agents are already active in your codebase
  2. Differentiating between assisted coding and autonomous agent workflows
  3. Recognizing the hidden technical debt from AI-generated code
  4. Assessing team reliance on AI for core implementation tasks
  5. Mapping AI tool usage across feature development and bug fixes
  6. Evaluating the impact of AI on code review velocity
  7. Understanding how AI alters debugging and incident response
  8. Tracking AI-generated code in version control history
  9. Measuring the proportion of agent-authored pull requests
  10. Observing changes in developer engagement patterns
  11. Documenting deviations from established coding standards
  12. Creating a baseline assessment of AI integration depth
Module 2. Reframing Engineering Leadership in the AI Era
Redefine leadership responsibilities when code is written by agents and developers become supervisors.
12 chapters in this module
  1. Shifting from code ownership to workflow stewardship
  2. Establishing accountability for AI-generated system behavior
  3. Redefining code review criteria for agent-authored logic
  4. Setting expectations for human oversight in automated pipelines
  5. Balancing innovation velocity with architectural integrity
  6. Communicating new escalation paths for AI-related failures
  7. Revising promotion criteria in an AI-augmented team
  8. Leading teams through identity shifts in developer roles
  9. Maintaining technical depth while delegating to agents
  10. Guiding teams on when not to use AI coding tools
  11. Building trust in systems you did not directly build
  12. Developing new KPIs for engineering effectiveness
Module 3. Auditing AI Integration Across the Development Stack
Conduct a comprehensive audit of where and how AI agents operate in your development environment.
12 chapters in this module
  1. Inventorying AI tools currently in use across teams
  2. Mapping AI activity across planning, coding, and testing phases
  3. Assessing integration depth in CI/CD pipelines
  4. Reviewing code generation patterns in service implementations
  5. Evaluating test suite creation by AI agents
  6. Auditing documentation generated from AI outputs
  7. Checking for unauthorized AI tool adoption in secure repos
  8. Analyzing dependency injection patterns from AI suggestions
  9. Measuring AI involvement in database schema changes
  10. Reviewing security scanning results on AI-authored code
  11. Tracking agent use in hotfix and incident resolution
  12. Documenting exceptions to manual coding standards
Module 4. Governance Models for AI-Generated Code
Design governance frameworks that ensure quality, security, and maintainability without stifling innovation.
12 chapters in this module
  1. Defining acceptable use cases for AI coding agents
  2. Establishing pre-approval requirements for AI-driven projects
  3. Creating code sign-off protocols for agent-authored modules
  4. Setting thresholds for human review intensity
  5. Implementing version control safeguards for AI changes
  6. Requiring provenance tracking in commit metadata
  7. Enforcing documentation standards for AI-generated logic
  8. Auditing third-party AI tools for compliance risks
  9. Building escalation paths for questionable AI outputs
  10. Integrating AI governance into existing architecture reviews
  11. Enabling rollback procedures specific to AI changes
  12. Designing audit trails for autonomous code decisions
Module 5. Evaluating Code Quality in Agent-Augmented Teams
Adapt code quality metrics and review practices to account for AI-authored logic.
12 chapters in this module
  1. Reassessing code complexity metrics with AI contributions
  2. Detecting overfitting in AI-generated algorithm implementations
  3. Evaluating readability of agent-produced code structures
  4. Measuring technical debt accumulation in AI-touched areas
  5. Assessing test coverage adequacy for AI-written functions
  6. Reviewing error handling patterns in autonomous code
  7. Validating adherence to internal design patterns
  8. Identifying duplicated logic across AI-generated modules
  9. Analyzing performance implications of AI-suggested architectures
  10. Checking for security anti-patterns in generated code
  11. Benchmarking AI code against manually written equivalents
  12. Establishing baseline quality gates for AI submissions
Module 6. Managing Technical Debt from Autonomous Coding
Identify, categorize, and mitigate the unique forms of technical debt introduced by AI agents.
12 chapters in this module
  1. Recognizing AI-specific debt in architecture decisions
  2. Tracking knowledge silos created by agent dependence
  3. Assessing maintainability of AI-generated code paths
  4. Measuring team understanding of agent-authored systems
  5. Evaluating long-term support risks in AI-built components
  6. Documenting assumptions embedded in AI-generated logic
  7. Prioritizing refactoring efforts in agent-heavy modules
  8. Building onboarding materials for AI-authored systems
  9. Creating runbooks for AI-maintained services
  10. Identifying undocumented coupling in AI-created interfaces
  11. Planning for agent deprecation and migration
  12. Establishing ownership of legacy AI-generated systems
Module 7. Aligning Architecture Governance with AI Workflows
Ensure architectural integrity when agents make implementation decisions independently.
12 chapters in this module
  1. Requiring architectural pre-validation for AI projects
  2. Setting boundaries for autonomous service creation
  3. Enforcing API design standards in agent workflows
  4. Reviewing data flow decisions made by AI agents
  5. Validating scalability assumptions in AI-generated systems
  6. Assessing observability implementation in agent-built services
  7. Enforcing consistency in error reporting structures
  8. Monitoring drift from approved architectural patterns
  9. Requiring human sign-off on infrastructure-as-code from agents
  10. Auditing technology choices suggested by AI tools
  11. Tracking compliance with data governance policies
  12. Integrating architecture reviews into AI deployment gates
Module 8. Securing Systems Built with AI Coding Agents
Adapt security practices to detect and prevent vulnerabilities introduced by autonomous code generation.
12 chapters in this module
  1. Assessing supply chain risks in AI-suggested dependencies
  2. Detecting hardcoded secrets in AI-generated configurations
  3. Reviewing authentication logic created by agents
  4. Validating input sanitization in AI-written endpoints
  5. Auditing role-based access control implementations
  6. Testing for injection vulnerabilities in agent code
  7. Monitoring for excessive permissions in AI-created services
  8. Enforcing secure coding standards in training data
  9. Scanning for license compliance in AI-recommended packages
  10. Evaluating cryptographic implementations from AI sources
  11. Establishing security baselines for agent outputs
  12. Creating automated security gates for AI pull requests
Module 9. Optimizing Team Structure Around AI Agents
Reorganize team roles, responsibilities, and workflows to complement rather than compete with AI capabilities.
12 chapters in this module
  1. Redefining developer roles in AI-augmented environments
  2. Structuring teams around AI supervision rather than coding
  3. Assigning AI stewardship roles within squads
  4. Balancing AI use across experience levels
  5. Designing onboarding for AI-native codebases
  6. Creating career paths for AI oversight specialists
  7. Coaching leads on managing hybrid human-agent teams
  8. Establishing norms for challenging AI suggestions
  9. Building feedback loops between developers and AI tools
  10. Measuring team velocity with AI contribution metrics
  11. Managing knowledge transfer in agent-dependent systems
  12. Planning for sustainable team composition
Module 10. Measuring Performance in AI-Integrated Development
Develop meaningful metrics that reflect both human and agent contributions to software outcomes.
12 chapters in this module
  1. Tracking feature delivery speed with AI attribution
  2. Measuring defect rates in AI-authored code sections
  3. Evaluating incident resolution time for AI systems
  4. Assessing rework frequency in agent-generated modules
  5. Benchmarking code stability across AI and manual work
  6. Analyzing test flakiness in AI-influenced pipelines
  7. Measuring deployment success rates for AI-built services
  8. Reviewing mean time to detect issues in AI code
  9. Calculating technical debt velocity in AI projects
  10. Correlating AI usage with system uptime metrics
  11. Auditing customer-reported bugs by origin source
  12. Creating balanced scorecards for hybrid teams
Module 11. Planning Phased Integration of AI Coding Agents
Develop a controlled rollout strategy that aligns AI adoption with organizational readiness.
12 chapters in this module
  1. Assessing team readiness for AI agent adoption
  2. Identifying pilot areas for controlled AI experimentation
  3. Setting success criteria for initial AI deployments
  4. Establishing feedback collection mechanisms from early users
  5. Evaluating infrastructure readiness for AI tooling
  6. Designing training programs for AI supervision
  7. Creating rollback plans for failed AI integrations
  8. Defining expansion criteria beyond pilot phases
  9. Integrating AI adoption into release planning cycles
  10. Aligning AI use with product roadmap priorities
  11. Planning for cross-team knowledge sharing
  12. Documenting lessons from initial AI implementations
Module 12. Sustaining Engineering Excellence with AI Agents
Build long-term practices that ensure continuous improvement and responsible evolution of AI-augmented development.
12 chapters in this module
  1. Establishing regular AI codebase health assessments
  2. Refreshing governance policies with operational feedback
  3. Updating training materials based on AI incidents
  4. Iterating on team structures as AI capabilities evolve
  5. Revising performance metrics to reflect new norms
  6. Conducting post-mortems on AI-related outages
  7. Sharing best practices across AI-augmented teams
  8. Planning for AI tool lifecycle management
  9. Evaluating new AI capabilities against existing standards
  10. Maintaining architectural vision amid rapid change
  11. Preserving engineering culture in AI-driven environments
  12. Documenting organizational learning from AI adoption

Frequently asked

Who is this course designed for?
Engineering leaders who own software delivery, architecture, and team performance in organizations adopting AI coding agents.
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Does this course cover specific AI tools or platforms?
No. The course focuses on organizational, operational, and leadership practices regardless of the underlying technology.
What formats do the templates come in?
The implementation playbook downloads as PDF and editable XLSX. The course reads in your learning environment and exports to PDF for offline use. The files are yours to keep.
Can I share this with my team?
The licence is per person. Team pricing opens from three seats: reply to the order confirmation with TEAM and we will set it up.
How quickly can I start?
The diagnostic is one sitting and the templates work straight out of the kit. Account access takes up to 24 hours rather than being instant, because every order is checked and updated against the latest sources before it is delivered.
$199 one-time. Approximately 3 hours per module, designed to be completed alongside regular responsibilities over 8 to 12 weeks..

Within 24 hours your account in the learning environment is provisioned and the tailored implementation playbook is delivered alongside it.

30-day money-back guarantee·Know your weakest area today·210 scored questions·Course included· Account access within 24 hours
30-day money-back guarantee, no questions asked.
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