Agentic Engineering Masterclass in Zurich

Bring your team in Zurich through the pipeline: how you plan, how you harness paid and open-source coding agents, and how you prove output before it reaches users. Two days. Humans on intent and users. Agents on implementation.

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Why Take This Workshop in Zurich?

Zurich hosts Google's largest engineering office outside the US, along with ETH Zurich, one of the world's top technical universities. The city's fintech and enterprise scene is deeply invested in type-safe development. The Agentic Engineering Masterclass in Zurich gives your team a shared pipeline from idea to users, so AI-assisted work stops being a personal habit and becomes how the squad ships.

Workshop at a Glance

Duration

2 Days

Format

Remote or On-site

Participants

Up to 12 participants

Price

CHF 611/person / day

What Your Team Will Learn

For engineering leads and developers who already ship with AI tools and need a shared pipeline from idea to users. You leave able to configure agents on a repo, plan as an artifact, and refuse to merge on a vibe.

A shared working model: humans own intent and the user bar, agents do the implementation
A harness on your repo: memory, skills, and tool choices that stay agent-agnostic
A quality pipeline that turns 'done' into evidence, not an 800-line diff
A way to run more than one agent without stepping on the same working tree
A written playbook your team can run on Monday

Workshop Curriculum

Two days on your (or a shared) codebase. Day 1 is environment, context, and planning. Day 2 is the quality pipeline: harness, parallel work, and evidence before a human looks.

Day 1. Environment, context, and how you plan

The working model: humans own intent and users, agents do the implementation
Paid and open-source harnesses: Cursor, Claude Code, Codex, OpenCode, and when each fits
Memory files: tiny global prefs, project memory that grows when you correct a mistake
Skills and progressive disclosure: knowledge you only load when the task needs it
Tool ergonomics: CLI vs MCP, token cost, latency, and why random skills can make agents worse
Plan as an artifact you can point at, not a wall of markdown
Hands-on: configure an agent on a real repo and run the first change through the plan

Day 2. Quality pipeline, parallel work, prove the output

When the agent says done: stop reviewing every diff, start reviewing evidence and risk
Quality pipeline: recover intent, rebase, adversarial review in a fresh context, end-to-end proof, docs, PR
Isolation for parallel work: worktrees, lifecycle, and how leads stop holding state in their head
Long-running loops with stop conditions: overnight work on verifiable objectives
Orchestration: one lead session that delegates, so you stay on direction instead of juggling every agent
Team playbook: shared skills, who reviews what, Monday rules for risk-based review
Hands-on: one change from idea to evidence to PR on your codebase

Tools & Focus Areas

Paid

Paid harnesses

Cursor, Claude Code, and Codex in practice. Same workflow, different tools. We stay harness-agnostic on purpose.

OSS

Open-source harnesses

OpenCode, Pi, and CLI-first setups so the workflow survives when the best model next month is not the one you bought this month.

Pipeline

Quality pipeline

Memory, skills, plan artifacts, isolated worktrees, adversarial review, and end-to-end evidence before merge.

Your Instructor

Davy Engone

Founder & Software Mentor @ Hackages

Davy has trained 5,000+ engineers. This masterclass is the workflow he is installing on real repos: plan hard, harness the tools, and keep humans on intent and users.