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AI Coding Assistants are everywhere - but (how) can they scale?
AI coding assistants have become extremely popular in a very short time. Tools like GitHub Copilot and Cursor are now part of daily work for many software teams. They help developers code faster, reduce repetitive tasks, and prototype ideas more quickly than ever before.
At the same time, more and more teams are asking a critical question: can AI coding assistants be used at scale - even in complex, business-critical software projects?
"Limitations" of Coding Assistants Today
AI coding assistants are powerful. And like any tool, they are not intended for every use case. If development teams scale their usage of coding assistants, some limitations may become visible:
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missing understanding of system architecture
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no awareness of domain boundaries or ownership
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fragmented code generation without coordination
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inconsistent output across modules and services
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security and compliance risks (causing costly governance and review loops)
Our Perspective: Coding Assistants are the future
We strongly believe in the power of AI coding assistants also in complex systems. The limitation is not the assistants themselves. The limitation is the missing structure.
That’s why we asked ourself:
What if coding assistants do not work alone, but together with a shared system design?
A Hackathon Insight: just a perfect pair
During a recent hackathon, we combined GitHub Copilot and Cursor with our next-generation architecture and design tool.
The result surprised us: it was a perfect match.
When coding assistants work side by side with structured design, everything improves:
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code becomes more consistent
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less time is spent fixing mismatches
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architectural drift is reduced
So we didn’t stop at a prototype.
From Experiment to Product Capability
We extended our product so that coding assistants and our Cloud Solution Workbench now work together seamlessly.
Instead of asking AI to “figure things out” from scattered context, development is now based on:
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a shared architectural model
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clear component boundaries
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a consistent design approach
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AI-readable structure
This gives AI what it was missing: Context. Structure. Constraints.
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Conclusion
AI coding assistants are not just a trend, they’re the new normal. But to use them at scale, teams need more than prompts. They need design.
Let's leverage the power of coding assistants by moving from prompt-first to design-first.
Frequently Asked Questions about this Blog
AI coding assistants work mainly on the level of files and code snippets. They do not understand system-wide architecture, domain boundaries or ownership models.
As systems grow, this may lead to:
- inconsistent implementations across modules,
- architectural drift,
- higher review and governance effort.
We believe AI coding assistants are the future of software development.
The "limitation" is not the AI. It is the missing structure around it.
Coding assistants become truly powerful when they operate within a shared design framework that provides:
- clear system boundaries,
- design consistency,
- architectural guidance,
- understandable context.
Structured design acts as a system-level navigation layer for AI.
By integrating coding assistants with our next-generation architecture and design tool, we enable AI to work with:
- shared architectural models,
- defined component responsibilities,
- standardized design patterns,
- AI-readable structure and constraints.
Instead of asking AI to guess, the system design tells AI how to behave.
