We build structured, human-in-the-loop development workflows where agents handle scaffolding, testing, and documentation. And engineers stay focused on the decisions that matter.
Swapping in a code completion plugin changes very little. Restructuring how work gets researched, planned, and executed changes a lot. Agentic Engineering is how we build software today.
Across client projects from commerce platforms to full website relaunches, our teams run an AI-first implementation model: agents drive the implementation, engineers drive the decisions. Delivery gets faster, quality gets more consistent, and every architectural decision has an audit trail.
We replace ad-hoc AI usage with a repeatable cycle: Research, Plan, Implement. Every non-trivial feature starts with a documented analysis phase before any code is written. A sub-agent validates the plan for gaps and scope creep. Implementation runs phase by phase, with human sign-off at each checkpoint.
We set up the agentic development workflow and hand it over. Your engineers get training on live tickets. We build the skills and commands for your stack, encode your conventions into the ruleset, and connect your tools. After that, the workflow runs without us.
Generic AI tooling doesn't know your codebase, your framework conventions, or your team's definition of done. We build project-specific skills — for static analysis, test generation, code review, and branch management — that encode your standards and run them automatically.
We connect Claude Code to your existing tools: Jira, GitLab, Confluence, your database, your CMS, your Figma files. Development context lives in the workflow, not in someone's head. Agents fetch ticket context, run queries, push to registries, and open merge requests without leaving the terminal.
Before code is committed, an agent reviews the diff for logic errors, performance issues, missing tests, and style violations. It handles branch naming, commit messages, rebasing, and pushing. Engineers review the findings, not the boilerplate.
For framework-specific work — Spryker module generation, migration analysis, pipeline diagnostics, test impact assessment — we build focused sub-agents that know the domain and stay within defined boundaries.
AI has no access to production systems. Deployment actions require explicit human approval. All generated code goes through standard review before merge. These rules live in the project configuration, not just in team agreements.
We start every project by defining an AI ruleset: coding conventions, architectural boundaries, test requirements, and tool permissions. These live in the instruction file that every agent reads before doing anything. The ruleset travels with the codebase and applies across the whole team.
Work runs in phases. Research surfaces decisions before implementation begins. Planning produces a structured, checkbox-driven plan that a second agent validates for completeness before engineers approve it. Implementation follows the plan phase by phase, with progress tracked in a file that also serves as the handover document. Engineers sign off at every checkpoint. Nothing deploys without a human decision.
We've run this on greenfield projects and on legacy codebases. It works in both directions.
The most interesting engineering work has always been architectural. With our agentic approach, that's where we put our focus, allowing us to produce visible results much faster.
Thomas Kühnel
Co-CTO
Turbine Kreuzberg
AI Tech Stack
We set up the full RPI workflow alongside commerce framework-specific agents that know facade patterns, transfer objects, and module architecture. Custom skills handle test generation and static analysis automatically.
We connect Figma, your CMS, and Claude Code via MCP servers. Agents extract exact design specs, generate components, and register them in server and client registries. Every component has a styleguide entry before it ships.
We audit your current setup, define the AI ruleset with your team, build the skills and commands that fit your stack, and run the team through the RPI cycle. The workflow stays with you after the engagement.
Tell us where your team is now, and we'll figure out where agentic workflows fit.
