AI-Native Engineering Training with Claude Code
A dedicated programme for enterprises and individuals to move from personal ad-hoc AI usage and vibe coding to Agentic Engineering at team and organization level.
Dedicated training for your organization, starting with at least 20 people
- 20+ practical tasks, targeted to your Product/Platform or provided by us
- Identifying and building your AI Champions Network
- Team Homework analysis provided by our experts
- Conducted in your ecosystem: recordings and supplementary materials stay with you
- Designed for Engineering organizations: Software Engineers, Architects, Quality Engineers, Data Engineers and other roles
Public training for smaller teams under 20 people and individuals
- 20+ practical tasks, bring your own repository
- Recordings are available for a recap, supplementary materials stay with you
- Designed for Software Engineers, Architects, Quality Engineers, Data Engineers and other roles
From Individual Usage to
Team-Scale AI-Native Engineering
Ad-Hoc
Structured
Spec-driven workflows, test-first discipline, safety nets in place
Integrated
CI/CD pipelines, oversight frameworks, team-wide conventions
Agentic
Multi-Claude orchestration, measurable velocity gains, continuous improvement
Ad-Hoc
Structured
Spec-driven workflows, test-first discipline, safety nets in place
Integrated
CI/CD pipelines, oversight frameworks, team-wide conventions
Agentic
Multi-Claude orchestration, measurable velocity gains, continuous improvement
Live facilitated sessions
4-5 hours per week of hands-on labs, group discussions, and practice with expert guidance.
Self-paced video lessons
2-3 hours of pre-session content covering core concepts and theory.
Real code, not toy demos
From Week 2, apply everything to your own production codebase. Bring Your Own Brownfield.
Trained by Practitioners
Our Trainers are ENDGAME AI-Native Practitioners whose mastery comes from 10s of successful engagements.
Hand-crafted materials
Shared Materials are hand-picked and crafted by ENDGAME Practitioners.
Homework evaluation
For Dedicated training our Practitioners provide a summary of a weekly team work.
Core Concepts
Prerequisites check
Git, terminal, code reading, and development fundamentals. Self-assessment quiz determines your track.
Tool setup
Node.js 18+, Claude Code, Git with worktree support, GitHub with Actions. Verify everything works on a sample repo.
Foundation videos
Four 30-minute modules: agentic AI concepts, how Claude Code works, AI across the SDLC, and ethics and responsibility.
Choose your codebase
Pick a production codebase for Weeks 2-4. Active development, reasonable complexity, some technical debt.
Labs & Practice
- Complete self-assessment quiz
- Install and verify all tools
- Run Claude Code on a sample repo
- Submit your brownfield codebase for approval
- Write a 500-word reflection on your current AI usage
Core Concepts
The agentic mindset
Context, reason, act, verify, repeat. Context engineering matters more than prompt wording. Explore-plan-code-commit workflow.
CLAUDE.md configuration
Project-level configuration files that tell Claude how to work. What to include, what to exclude, and where to put them.
Prompt patterns & token economics
Imperative, exploratory, constrained, verification, and decomposition patterns. Managing context size and cost.
Systematic exploration
Architecture first, then flows, then edge cases. Generate documentation as you explore.
MCP servers
Connect Claude to external systems: databases, APIs, browsers, documentation. The screenshot-iterate loop for UI development.
Custom skills & hooks
Skills in .claude/skills/, hooks for lifecycle events. Automate formatting, linting, and dangerous command blocking.
Labs & Practice
- Explore a sample repo and document three non-obvious insights
- Create and refine a CLAUDE.md file, test effectiveness
- Apply prompt patterns to 10 tasks
- Create architecture documentation for a legacy codebase
- Configure MCP servers (filesystem, Playwright)
- Create a custom skill with documentation
- Draft a CLAUDE.md for your brownfield codebase
Core Concepts
Why specifications matter
Vibe coding fails at scale. Specs are the single source of truth for both you and the AI. Functional requirements, acceptance criteria, constraints.
Three intensity levels
Spec-first (complete before coding), spec-anchored (living document), spec-as-source (humans edit specs, AI generates code). Match intensity to task size.
The four-phase workflow
Specify (what, not how), Plan (stack, architecture, constraints), Tasks (small, reviewable chunks), Implement (one task at a time, commit often).
Human review gates
Add judgment between phases. The compound error problem: 100 steps at 1% error rate = 63% failure probability. Gates interrupt the cascade.
EARS notation
Five patterns for unambiguous requirements: ubiquitous, event-driven, state-driven, unwanted behaviour, and optional.
SDD tools
Spec-Kit, OpenSpec, BMAD Method, or the manual four-file approach. Pick what fits your context.
Labs & Practice
- Expand a vague feature request into a full specification
- Execute the four-phase workflow end to end
- Convert requirements into EARS notation
- Practice the iterative workflow: change, verify, commit, repeat
- Write a specification for a pending feature in your codebase
- Apply the workflow through Phase 3 on your codebase
Core Concepts
TDD with AI
Write tests from input/output pairs first. Confirm they fail. Then implement. Verify in a fresh session to catch overfitting.
Understanding legacy code
AI gives a head start but lacks domain expertise. Watch for shepherding, drifting, and the illusion of competence.
The 7-step refactor loop
Set the scene, plan first, wrap in tests, propose surgical edits, review diffs, tight loop, land with context. Zero regressions.
PAID framework
Prioritise (high debt + high value), Address (low debt + high value), Investigate (high debt + low value), Document (low debt + low value).
Security & compliance
OWASP Top 10 review. Secret management. Licence compliance. Constitution files in CLAUDE.md. Audit trails.
Diagnosing AI failures
Context pollution, prompt ambiguity, knowledge gaps, pattern mismatch. The Thread Fold technique for recovery.
Labs & Practice
- Implement a feature with strict TDD, verify in fresh context
- Generate characterisation tests for undocumented legacy code
- Conduct a security audit of AI-generated code
- Refactor a high-complexity module using all 7 steps
- Categorise technical debt using the PAID framework
- Add characterisation tests to a low-coverage module in your codebase
Core Concepts
CI/CD integration
GitHub Actions with claude-code-action for PR reviews. Headless mode for automation. Automated gates: coverage, docs, breaking changes.
Multi-Claude patterns
Orchestrator-worker, writer-reviewer, parallel execution with Git worktrees. Team coordination through shared CLAUDE.md conventions.
Oversight & trust calibration
Human-in-the-loop, human-on-the-loop, autonomous with audit. Match oversight level to task risk: HIGH, MEDIUM, LOW.
Advanced customisation
Skills, hooks (14 lifecycle events), custom agents. Combine narrow scope, appropriate model, and minimal permissions.
Session mastery
Stay below 70% context window. Strategic model selection: Opus for reasoning, Sonnet for coding, Haiku for exploration. The Checklist Method.
Engineering at scale
Plugins, Claude Code SDK, Agent Teams. From personal CLI to team platform.
Labs & Practice
- Configure a CI/CD pipeline with PR review and coverage analysis
- Execute the writer-reviewer pattern on a codebase
- Build a PreToolUse guard hook
- Complete the trust calibration exercise
- Draft a CI/CD integration plan for your codebase
- Design an oversight framework for your team
Dedicated
€40,000 to €100,000
Up to 40 to 100 engineers
UP TO 50% OFF
Public
€2,000
per seat
25% OFF
1–24 seats
€1,750
25–49
€1,500
50+
€1,250
You should be comfortable with the following before starting the programme.
Git
Branching, merging, handling conflicts
Terminal
Comfortable working in a command line
Code
Proficient in at least one programming language
Fundamentals
Basic software development concepts