Enterprise Archi Principles: High

๐Ÿ’ก Enterprise Archi Principles: High โ€” Executive Summary

Technical architectural analysis, practical configuration manifests, and deployment instructions for Enterprise Archi Principles: High.

๐Ÿ“‘ Table of Contents

Business

Repository

Architecture & System Flow Diagram
Architecture & System Flow
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Application


Cluster Overview

Architecture & System Flow Diagram
Architecture & System Flow
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GitOps

Architecture & System Flow Diagram
Architecture & System Flow
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CI/CD

Architecture & System Flow Diagram
Architecture & System Flow
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Jenkins CI

Architecture & System Flow Diagram
Architecture & System Flow
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North-South Traffic-flows

Architecture & System Flow Diagram
Architecture & System Flow
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East-West Traffic-flows

Architecture & System Flow Diagram
Architecture & System Flow
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GitOps


Node-self Helmfile

Architecture & System Flow Diagram
Architecture & System Flow
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ArgoCD

Architecture & System Flow Diagram
Architecture & System Flow
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CICD

Architecture & System Flow Diagram
Architecture & System Flow
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Monitoring


Thanos Deployments

Architecture & System Flow Diagram
Architecture & System Flow
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Extra

Hardware


Networks

Architecture & System Flow Diagram
Architecture & System Flow
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E2E Testing tool

Architecture v1 (backend dependent) + API token

  • Suggestions for existing architecture problems
    1. Necessity of selecting AI service call method (Agent VS API)
    1. Optimality of Harness Engineering in the Agentic AI Environment
      1. Harness technology is a core optimizer that helps AI agents self-correct errors and reliably control local tools
    2. Licensing structure issues
      1. โ€˜LLM API Tokenโ€™ and โ€˜Agent AI Asset (Solution Framework)โ€™ have an independent licensing system that is completely separate conceptually and contractually.
    3. Server operation risk
      1. Unlike stable one-time API calls, when running AI that thinks and acts directly on the server, the risk of system down due to server overload and infinite loop is very high
    4. WebSocket Transmission
    5. Real-time screen streaming structure using web sockets is difficult to implement technically and consumes a lot of development resources.
Architecture & System Flow Diagram
Architecture & System Flow
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Architecture v2 (Local Packaging) + Local Agentic AI

  • Architecture Features
    1. Provide npm-based local initialization
    1. Provide agent options
      1. Provides playwright and agent installation
      2. Licensing
      3. Basic harness configuration pre-complete
    2. Optional: Call the backend and load existing harness settings, etc.
    3. Integration (automation) of Chrome extension distribution
    4. The backend exists for log recording, migration, and inquiry
    5. Actual execution is performed by calling the agent on localhost.
    6. You can also operate the backend directly from localhost. (management page)
    7. Uses playwright directly locally by playwright-mcp, so headed tests are stable
  • Threshold
  • Inference calculations use the cloud, so they do not require a GPU, but they basically look for the specifications of the test terminal.
    • cpu/ram
Architecture & System Flow Diagram
Architecture & System Flow
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Application

GitOps

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