Most AI coding demos stop at the diff.
This one doesn’t.
In this workshop we wire up a complete, governed software development lifecycle in which every moving part stays inside mittwald. The model that reasons about your code is a mittwald-hosted model. The platform that gets provisioned, deployed, and configured is mittwald. And the workflow that keeps the whole thing accountable is Spec Kitty.
Put plainly: mittwald’s inference drives mittwald’s platform — and Spec Kitty makes sure it happens in the right order, under your rules, with an audit trail you can hand to a reviewer.
What we plug together
mittwald AI Hosting — GDPR-compliant inference from German data centres over an OpenAI-compatible API. Qwen3-Coder-30B for implementation, gpt-oss-120b for reasoning and agent work, Ministral-3-14B for text and vision,
Qwen3-Embedding-8B behind RAG. No US data transfer, no prompt retention, no external providers in the chain.
The mittwald MCP server — your agent’s hands on the platform. Projects, apps, databases, domains and SSL, mail, security, backups, automation: all reachable in natural language, authenticated with OAuth 2.1 or an API token for CI. Not a wrapper around a few endpoints — full platform coverage.
Spec Kitty — the governance and workflow layer. A project Charter that encodes your rules, doctrine that carries your team’s hard-won conventions, specs and plans that survive a context window, work packages in isolated git worktrees, a deterministic runtime that decides the next step, and fresh-model plus human review before anything merges.
What we actually do together
1. Stand up a project Charter and doctrine — your architecture, security, and review policy written down where an agent will actually read it.
2. Point the agent surface at mittwald AI Hosting and connect the MCP server. First run: the agent inspects your live infrastructure and tells you what’s there.
3. Take one real feature from vague intent through specify → plan → tasks, with ambiguity surfaced instead of guessed.
4. Run implementation as parallel work packages in isolated worktrees, with a mittwald-hosted coding model doing the work.
5. Review with fresh eyes, accept, merge — then let the agent provision, deploy, and configure the result on mittwald through MCP.
6. Close the loop: mission review and retrospective, so what the team learned in this mission changes doctrine for the next one.
What you take home
A working, reproducible setup: Spec Kitty + mittwald inference + mittwald MCP on your own machine and your own project.
A Charter and doctrine starting point you can adapt to your stack — including legacy and vendor-specific constraints.
A defensible answer to the question your compliance people are going to ask: where did the code come from, who approved it, and did any of this leave the EU?
A deployment path where the agent doesn’t hand you a README — it does the provisioning.
Who this is for
Agencies and product teams shipping software that has consequences — customer systems, shops, platforms, regulated workflows. Developers who are already using AI agents and are tired of reviewing their output blind. Technical leads and CTOs who need speed without giving up control. Anyone who has to answer for digital sovereignty in writing.
Bring: a laptop, a project you actually care about, and we will provide mittwald demo accounts.
Robert Douglas
Robert has spent decades building and scaling software organizations. Spec Kitty comes directly from seeing how easily intent drifts once teams, tools, branches, and AI agents all start moving at once.