We want to use AI productively
AI assistants and agents can accelerate development. Without clear quality standards, architectural boundaries, and accountability, however, they also accelerate defects and technical entropy. We combine practical adoption with clear guardrails.
Discuss AI adoptionDoes this sound familiar?
There is no path from experimentation to dependable practice
- Individuals already use AI, but shared rules are missing.
- The quality and origin of generated changes are difficult to trace.
- Uncertainty about privacy, security, and suitable tools is slowing adoption.
- Productivity claims cannot yet be assessed realistically.
What matters now
Combine speed with engineering discipline
Your team learns to divide work appropriately between people and AI, provide context in a controlled way, and safeguard results through tests, reviews, and static analysis. Accountability and decision-making remain with people. Our AI manifesto describes the principles behind this approach.
Guidance
Engineering experience for AI adoption
Sebastian Bergmann and Stefan Priebsch connect AI-assisted development with software quality and architecture.
From individual experiments to a controlled way of working.
Together, we clarify where AI can help today and which guardrails your team needs.