Master of AI-Augmented Coding
Skills, MCPs, Subagents, Multi-Level Orchestration, Parallel Execution
Transform your development workflow completely. Stop prompting and start orchestrating. This intensive workshop teaches you to build systems, not just write code with AI assistance.
One method, several ways in: in-person team workshops, individual tracks, and a self-paced cohort. Engineer and pilot tracks run side by side: engineers build the setup, business users pilot a pre-built one.
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Curious What Stage of AI-Augmented Coding You're At?
Download our free guide and discover where you stand on the path from prompting to orchestrating.
Day by Day Breakdown
Each day builds on the previous, taking you from foundations to mastery.
Foundations & Your Skills Layer
Go from prompting and hoping to controlled, predictable workflows on a real repo.
What you'll learn
- What Claude Code and the agentic paradigm really are, and how tokens and context windows actually work
- The mindset shift from writing code to orchestrating it
- Keyboard shortcuts, operating modes, and slash commands that keep you in control
- Project memory with CLAUDE.md, file imports, the project/user/local hierarchy, and context priming
- MCP servers: extending Claude's reach without polluting context
- When a reusable skill (SKILL.md) makes sense versus a command, and how triggers fire
- A first taste of subagents: a skill that spawns one
What you'll practice
- Onboard onto an unfamiliar repo and have Claude map its architecture, features, and data flow
- Add a real feature on a practice repo: scope it, constrain it, build incrementally
- Write your own skill, run ours, then compose skills and discover their limits
- Drive commits, /review, PR descriptions, and merge conflicts through Claude
- Practice prompting on the repo and see the non-determinism demo firsthand
You leave with
- A working personal setup with your own commands, project memory, and first skills
- A reusable skills layer you wrote yourself and can run from day one
- A clear map of the anti-patterns to avoid
- Your shared training repo evolves from glider to plane, carrying your new setup
System Layer & Your First Orchestrator
Stop using small tools one by one. Start building a unified system on your real project.
What you'll learn
- The Skill / Agent / Hook architecture, and when to reach for each
- Subagents and the orchestrator-worker pattern, including parallel execution
- Advanced skills: auto-discovery, monorepo layout, and composition
- Agentic thinking: why everything is markdown and how a unified system beats scattered tools
What you'll practice
- Build your first subagent plus a safety hook with enforcement thinking
- Build your first orchestrator on YOUR real codebase
- Work through real-world friction: agent debugging, terminology mapping, and runaway-process control
- Use /insights to analyze your sessions and get personalized recommendations
You leave with
- Your first working subagent, safety hook, and orchestrator (your repo evolves plane → rocket)
- A repeatable system-building cycle instead of one-off prompting
- A personalized set of next-step recommendations from /insights
Multi-Level Orchestration & Parallel Execution
Run a system of agents that builds real software at high quality, safely and in parallel.
What you'll learn
- The three-layer orchestration model (task, capability, and surface), plus the gate-check for when not to orchestrate
- Nested subagents: the boundary as a choice, with a middle layer as a firewall around its workers
- Self-correcting orchestrated loops: the verification mechanisms and contracts that make orchestrators converge
- Hooks and security: the full enforcement architecture with permissions, deny rules, and sensitive-file protection
- How to lay out architecture, platform features, and folders for a system that scales
- Agentic teams (experimental): a peer-to-peer mailbox and shared task list, demo-only
What you'll practice
- Rewrite your app into a multi-level orchestration system and run the whole machine end-to-end
- Run agents in parallel with git worktrees and isolation: worktree
- Let it run safely against a read-only database
- Bonus: wire up voice notifications when a long run finishes
- Capstone: design a full agent system with /design-agent-system
You leave with
- A multi-level, self-correcting, parallel setup you can diff and reuse
- A clean folder and architecture layout for your agent system
- A designed agent system from the capstone
- Your repo reaches station, the finished multi-level system
Your Data & Company-Wide Infrastructure
Turn the flood of data AI creates into something your company can use, then turn one engineer's setup into shared infrastructure with an operating model the whole company can adopt.
What you'll learn
- The data-noise problem: AI creates more tickets, docs, logs, and transcripts than anyone can read, and how micro-tools put them to work
- When an MCP beats a skill, a script, or a CLI: the decision rubric
- RAG and vector stores: embeddings, chunking, and retrieval, and which projects genuinely need it versus where agentic search already wins
- Workflows: deterministic, coded multi-agent orchestration, with the pattern toolkit and convergent-loop harness as take-home
- Architecture and standards for a shared setup: org, team, project, and personal layers, and the standards that prevent drift
- The AI-augmented team: what each role can do, review gates, trust levels, and how agentic work is managed and measured
What you'll practice
- Spec your company's first micro-tool, then build and register your own MCP from the provided scaffold, scoped to one subagent
- Wire retrieval over a document corpus into your setup with embeddings and a vector store
- Publish a plugin to a private marketplace and have a teammate install what you publish
- Map your own team onto the new operating model: roles, review gates, and trust levels
- Regression-check a .claude/ setup, then run /insights and propose CLAUDE.md edits
- Capstone: draft your adoption blueprint, from pilot to champions to the whole org on a 30/60/90 plan
You leave with
- Your own team MCP built from the provided scaffold, plus the MCP-vs-skill-vs-script decision rubric
- The RAG starter: a chunk-and-embed script and a retrieval skill, plus the RAG-vs-agentic-search rubric
- A published plugin and a private-marketplace template
- The .claude/ governance and evaluation checklist, plus the adoption-blueprint template
- The take-home pattern toolkit: Workflows references and a reusable convergent-loop harness
Learn From the Experts
Adam Bilišič
Founder & AI-Augmented Coding Expert
English, Slovak
12+ years of professional experience. Ex-CTO of a Swiss company with 8+ years leading teams for enterprise clients.
Jerguš Frajt
Founder & Senior AI Engineer
English, Slovak
10+ years as a full-stack engineer. Delivered complex solutions for Swiss enterprise clients. Expert in AI systems and development workflows.
Choose your AI-Augmented Coding level.
Company-wide capability, from a first agentic setup to a rollout your whole company can share and maintain. Every engagement starts with a free pre-assessment, so no one pays for what they already know.
Foundations
Days 1 + 2plus travel expenses
You leave with a personal agentic setup (rocket).
What you leave with
- Your own slash-commands, memory, and first real skills
- A first subagent, a safety hook, and your first orchestrator
- Skill, agent, and hook architecture on a real repo
- Built hands-on, then applied to your own projects
Day by day
Day 1
Foundations, commands and memory, MCP servers, your first real skills, and a first subagent.
Day 2
Skill, agent, and hook architecture, your own subagents plus a safety hook, and your first orchestrator.
Orchestration
Days 1 to 3plus travel expenses
You leave an orchestrator (station).
What you leave with
- Everything in Foundations
- The three-layer orchestration model (task, capability, and surface), plus the gate-check for when not to orchestrate
- Nested subagents with a middle layer that firewalls its workers
- Self-correcting loops with the verification mechanisms and contracts that make them converge
- Parallel execution across git worktrees, run safely against a read-only database
- The full enforcement architecture: permissions, deny rules, and sensitive-file protection
- Capstone: your app rewritten into a multi-level system with /design-agent-system
Day by day
Day 1
Foundations, commands and memory, MCP servers, your first real skills, and a first subagent.
Day 2
Skill, agent, and hook architecture, your own subagents plus a safety hook, and your first orchestrator.
Day 3
The three-layer orchestration model (task, capability, surface) and the gate-check for when not to orchestrate, nested firewalled subagents, self-correcting loops with verification contracts, parallel git-worktree execution, and the full enforcement architecture.
Advanced & Infrastructure
Days 1 to 4plus travel expenses
You leave as the person who takes it company-wide: the flood of data AI creates put to work, and a setup the whole company can share and maintain.
What you leave with
- Everything in Orchestration
- Utilize the data noise AI creates: micro-tools over tickets, docs, and schema, narrow bounded surfaces that cut mental load
- Build your own MCP hands-on, plus the rubric for when an MCP beats a skill, a script, or a CLI
- RAG and vector stores hands-on: embeddings, chunking, and retrieval, and which projects genuinely need it
- Distribute your setup via plugins and a private marketplace, and a teammate installs what you publish
- The AI-augmented team: what each role can do, review gates, and trust levels, plus governance and enforcement across teams
- Capstone: your adoption blueprint, a 30/60/90 rollout plan, plus knowledge-base artifacts and the take-home pattern toolkit
Day by day
Day 1
Foundations, commands and memory, MCP servers, your first real skills, and a first subagent.
Day 2
Skill, agent, and hook architecture, your own subagents plus a safety hook, and your first orchestrator.
Day 3
The three-layer orchestration model (task, capability, surface) and the gate-check for when not to orchestrate, nested firewalled subagents, self-correcting loops with verification contracts, parallel git-worktree execution, and the full enforcement architecture.
Day 4
Micro-tools that turn the data noise AI creates into something you can use, building your own MCP and wiring RAG hands-on, distribution via plugins and a private marketplace, the AI-augmented team operating model, plus governance and your 30/60/90 adoption blueprint.
Per group of up to 10 engineers. Plus 10% per extra engineer. Plus 50% for a second facilitator on large-group days. Travel billed at cost. All prices ex-VAT.
From workshop to the way your team ships
Post-workshop guidance with weekly challenges applied to real shipping work, until agentic coding is simply how your team ships.
- Weekly live guidance and challenge design on real work
- A shared, governed setup stood up on your stack
- Tangible weekly progress, ROI compounds during the engagement
Not an engineer? You still ship real work.
In our company workshops we run a parallel pilot track for business users. Non-technical people (product owners, marketing, operations) pilot agentic setups our engineers prepare, so they ship reliable results without deep coding. This is augmented building with guardrails, not vibe-coding that falls apart next week.
Built on engineer-grade setups
You pilot pre-built agents and skills our engineers configured, so the foundation is solid before you touch it.
Ship from day one
Automate the tedious parts of your daily work and build small internal tools that actually hold up.
No computer-science degree required
You learn to operate the system, not to write it from scratch. Clear guardrails keep you safe.
What non-engineers said
“As a PO, I now know how to use Claude in my daily work and improve efficiency and accuracy tremendously. Full recommendation!”
“A lot to learn, even for a project manager without coding experience.”
Get your team orchestrating AI systems while others are still vibe-coding.
After a free 30-minute skills map, we take your developers to multi-level orchestration and secure, parallel delivery, at the quality standard you already expect. No pitch.