Architecture | KIM3310 Systems

System Architecture - doeon-kim-portfolio

This document is the system-level architecture attachment for the repository. It keeps the technical stack, runtime boundary, data/control flow, deployment surface, and operating assumptions in one place.

Architecture Summary

Repository-local proof surface for operations control surfaces and reliability automation, backed by Node/TypeScript runtime, GitHub Actions validation.

Runtime And Data Flow

flowchart LR
    User["User or technical reviewer"] --> Surface["Public demo, CLI, package, or README surface"]
    Surface --> Runtime["Runtime boundary: Node/TypeScript runtime, GitHub Actions validation"]
    Runtime --> Control["Control plane: configuration, policies, adapters, and jobs"]
    Control --> Data["Data and artifacts: fixtures, reports, logs, exports, or model outputs"]
    Runtime --> Observability["Observability and validation hooks"]
    Observability --> Handoff["Documented handoff and operating boundary"]
    Data --> Handoff

Primary domain: operations control surfaces and reliability automation.

Stack Surface

Cloud Or Local Deployment Boundary

Operating model: event-driven control planes, observability-first services, SLO dashboards, and resilient data stores

Deployment patterns

• Event-driven control surface with telemetry, escalation, and operator handoff states

Control boundaries

• identity boundary and least-privilege service access
• environment separation for local, staging, and managed runtime paths
• secret storage outside source and deterministic fallback for missing credentials
• observability hooks for logs, metrics, traces, and audit events
• rollback path for deployment, schema, and model changes

Resilience controls

• bounded retries with explicit failure states
• health/readiness checks before operator-facing flows are trusted
• idempotent data or artifact writes where repeat execution is possible
• cost and quota guardrails for hosted services and model adapters

AI And Automation Boundary

Operating model: incident summarization, action recommendation, anomaly explanation, operator handoff, and replayable decision support

Engineering patterns

• Turn telemetry into summaries, incident narratives, action queues, and shift handoff artifacts
• Preserve the source event trail beside generated explanations for fast correction and audit
• Separate deterministic checks from model-generated output so the system remains testable without external credentials
• Capture prompts, inputs, outputs, and decision metadata as inspectable artifacts instead of hidden side effects
• Gate model-assisted actions with policy, confidence, and fallback states before they reach an operator path

Evaluation and model-risk controls

• deterministic fixtures for CI-safe verification
• golden output or schema checks for generated artifacts
• trace capture for prompts, tool calls, inputs, and outputs
• quality gates that fail closed when evidence is missing

Risks to keep explicit

• stale telemetry
• opaque recommendations
• handoff loss
• weak rollback or escalation paths

Attached Architecture References

• Service architecture
• Cloud + AI architecture
• Architecture manifest
• Product operating model
• Quality gate

Local Architecture Guard

python3 scripts/validate_architecture_blueprint.py

CI workflow: .github/workflows/architecture-blueprint.yml.

Update this document whenever runtime entrypoints, data stores, hosted services, model/provider boundaries, or operating assumptions change.