G – Conclusions & How This Was Made
G1 · Overall Assessment
Kernaro met and in some areas exceeded expectations for a Beta product.
It represents a genuinely new level of architecture management – not a chat assistant bolted onto a tool, but an AI layer that understands the live model, can act on it, and can be governed through event-driven agents.
From a competitive perspective:
- Kernaro supports active model creation and modification
- Event-driven automation, and the entire SDLC scope at a fraction of the price of comparable EAM platforms.
- Competitors like Bizzdesign [1] currently offer read-only AI assist at a cost that is at minimum 5× higher, while covering only the CxO layer of the SDLC.
Key insight from this test:
AI will not replace architects, analysts, or developers.
It will amplify those who have built the systematic foundations beneath it –
TAXONOMY, ONTOLOGY, and role-based VIEWS.
Those who have not will expect AI to substitute for that work. It cannot.
G2 · CAA – Context Aware Approach
The most important principle demonstrated by this entire test is Context Aware Approach (CAA).
Before any tool was evaluated, context was defined:
1. Why are we digitising reality?
→ To support understanding and decision-making across SDLC roles.
2. What problem does Kernaro solve?
→ Communication gap between model specialists and non-specialist stakeholders.
3. What is the test scope?
→ Beta evaluation on a controlled QEA model (Coffee Machine specification).
→ Agents, JavaScript generation, APV methodology integrity.
4. What are the constraints?
→ Data privacy (no production data to external LLM without compliance review).
→ Budget ($20 API credits).
→ Time (Beta extended to April 30, 2026).
Only after this context was established did the actual testing begin.
The output of a tool, an AI, or any SDLC process is only as good
as the context that was defined before it started.
This is not a new principle. It is the same discipline that separates a well-scoped project from a runaway one. CAA simply names it explicitly and applies it consistently.
G3 · How This Report Was Made – KNIFE + Claude + OneNote
This report did not emerge from a single session. It is the output of an iterative process that mirrors standard SDLC practice.
Tools used
| Tool | Role |
|---|---|
| OneNote | Continuous notes during testing – raw observations, screenshots, error messages, timestamps |
| Claude Sonnet 4.6 | Knowledge partner – analysis, prompt engineering, debugging, structured writing |
| KNIFE framework | Knowledge structure – each section is an independent, reusable knowledge unit |
| Kernaro Beta | The subject of the test – also used to generate some of its own test material |
Process
1. CONTEXT DEFINITION
Define why, what, for whom, under what constraints
→ OneNote: initial scope and goals
2. EXPLORATION
Run tests iteratively, note findings as they happen
→ OneNote: raw log with timestamps and screenshots
3. ANALYSIS WITH CLAUDE
Process raw notes → identify patterns, classify findings,
draft structured content, debug agent prompts
→ Claude Sonnet 4.6: several conversation iterations
4. KNIFE STRUCTURE
Organise output into independent, reusable sections
→ Central index + sub-files A–G
→ Each section can stand alone or link to others
5. REVIEW & CORRECTION
Check against actual screenshots and test results
→ Corrections applied per section (e.g. wrong tools list, MariaDB vs SQL Server)
6. PUBLISH
Send to Sparx Systems for feedback → LI post series → GitHub KNIFE (public)
This is SDLC applied to knowledge production. The same discipline that governs software delivery governs the creation of this document.
KNIFE as a publication framework
KNIFE (Knowledge Is Not For Everyone, or simply: shareable knowledge units) is a personal framework for capturing and publishing practical knowledge from real work.
The approach:
- Each KNIFE is a self-contained, linkable unit
- Written from practice, not from theory
- Published openly on GitHub (public, Like & Share license)
- Designed to be referenced in LinkedIn posts, presentations, and client work
This Beta test report is KNIFE #103 (Kernaro AI Beta). It references and will be referenced by related KNIFEs on CAA, SDLC tooling, and prompt engineering.
The goal is not to produce content for content's sake.
The goal is to demonstrate systematický prístup – the value of systematic thinking –
through real, verifiable examples.
If that attracts partners, clients, or collaborators, the approach validated itself.
G4 · What Was Not Tested
For completeness – areas explicitly out of scope for this Beta:
- Prolaborate + Genie AI integration
- EA Native AI Assist (OpenAI / Gemini) – comparison test
- JP MCP Server (Sparx Systems Japan) – practical setup
- Trerado – not yet released
- Azure OpenAI backend for Kernaro – enterprise compliance path
- Python Execution via Kernaro – Script Agent defaulted to JavaScript
- UC-02 and beyond in APV methodology integrity check
These remain as candidates for future KNIFE articles and tests.
G5 · Next Steps
| Priority | Action |
|---|---|
| Immediate | Send this report to Sparx Systems (kernaro@sparxsystems.com) |
| Short term | LI post series (3 posts: hook → technical → conceptual) |
| Medium term | Complete KNIFE #103 with sections A, B + images |
| Medium term | Draft KNIFE: Digitalizácia reality – Prečo a Ako |
| Later | Test JP MCP Server + Claude Desktop |
| Later | Evaluate Kernaro + Azure OpenAI backend for enterprise PoC |
Product & Reference Links
[1] Bizzdesign Horizzon – https://bizzdesign.com
[2] Kernaro AI for EA – https://kernaro.sparxsystems.com
[3] Anthropic Console – https://console.anthropic.com
[4] KNIFE repository – http://knifes.systemthinking.sk
[5] SystemThinking – https://systemthinking.xyz