Foundations of AI Augmented Architecture with Kernaro Assist and Sparx EA
Kernaro Assist puts AI directly inside Enterprise Architect — ask questions, author elements, generate diagrams, and validate standards without leaving the tool. This private training course teaches your team to do real AI augmented architecture with it, on your own models and standards.
Format: private team training course · Hands-on, on your repository · Prerequisites: Sparx EA with Kernaro Assist
We start by understanding your context
This isn't generic training. Before we begin, we meet with your architecture leader to understand the context your team works in — your tools, your processes, your repository structure, and the use cases you've prioritized. Then we tailor the course content to fit, so your architects learn AI Augmented Architecture on the way your practice actually works — not in the abstract.
Access isn't capability
Enabling Kernaro Assist doesn't make a team good at AI augmented architecture, any more than opening Sparx EA makes someone an architect. The add-in amplifies expertise; it doesn't manufacture it. To get reliable results, an architect has to know how to direct the work — what to model, what standards to check, what "good" looks like in their repository.
That's what this course builds. Not button-clicking — the craft of working with an in-tool AI co-pilot that takes your direction, acts on your live model, and handles the mechanical steps, while your judgment stays in the loop. (To be clear: Kernaro Assist is a Sparx Systems product. We teach it; we don't make it.)
What the course covers
Kernaro Assist, inside EA
Working fluently with the AI co-pilot built into Enterprise Architect: natural-language Model Chat to explore the repository, and directing in-tool actions without writing complex queries or leaving the modeling environment.
Your repository & MDG
Kernaro Assist works on live models, respects EA structures, and aligns with your MDG and governance. We practise against your running repository and your own standards — not a generic default.
The four use cases
Modeling, analysis, governance, and stakeholder engagement — practised on your real models against your own standards, so the value shows up in your work, not a demo.
Module by module
Orientation & Model Chat
Confirm Kernaro Assist is installed, enabled, and connected to your repository. Understand the architect-drives/AI-executes model, and use natural-language Model Chat to explore your architecture and get context-aware answers — your first guided task end to end.
Core modeling on real inputs
Create, modify, and query elements directly in EA using natural language. Author and evolve content while maintaining structural integrity and MDG compliance. Work within your standards, not a generic default — so what the AI produces is review-ready in your repository.
Analysis & governance
Use diagram and model analysis to surface relationships, dependencies, and hierarchies; then validate against your defined standards and rules to find inconsistencies, missing relationships, and naming issues. Where automation confirms completeness — and where you confirm correctness.
Stakeholder engagement & habit
Turn the model into business-readable outputs stakeholders can act on, and build the working habits that make this stick after the training ends. We'll also touch on where event-driven agents and Kernaro AI Hub extend the practice for teams that need governed, shared scale.
Why this beats generic AI training
A generic AI course teaches you to chat. This teaches you to do architecture — inside the tool you already model in, on your repository, in your MDG, with your judgment in the loop. Because Kernaro Assist acts directly on live EA content and respects your standards, the work you do in the course is real work, not exercises. You leave with a portfolio of model changes, analyses, and validations produced during the program, and the habits to keep going.
Other Foundations
Bring the course to your team.
A conversation first — we'll confirm prerequisites and scope a course to your repository and goals.
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