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System Architecture - dream-interpretation-pages

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

Area Design
Repository dream-interpretation-pages
Primary domain agent runtime reliability and AI workflow orchestration
Primary stack Node/TypeScript runtime, GitHub Actions validation
Architecture axes cloud architecture, AI engineering, reliability, security, operator experience

Repository-local proof surface for agent runtime reliability and AI workflow orchestration, 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
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Primary domain: agent runtime reliability and AI workflow orchestration.

Stack Surface

Layer Current surface Operating note
Interface Public demo, README, CLI, package, or static proof surface depending on repository shape Keep the first screen or command path inspectable without private credentials.
Runtime Node/TypeScript runtime, GitHub Actions validation Keep runtime adapters bounded by environment configuration and documented fallbacks.
Control plane Policies, configuration, job orchestration, tests, and release scripts Keep operator-impacting changes traceable through docs and validation hooks.
Data and artifacts Fixtures, generated reports, screenshots, exports, logs, or model outputs Keep sample and generated artifacts clearly separated from private or customer data.
Operations CI, local validation, architecture guard, and handoff notes Keep the architecture docs current when runtime, data, or deployment boundaries change.

Cloud Or Local Deployment Boundary

Operating model: stateless runtimes, provider adapters, queue-aware execution, telemetry, and controlled secret boundaries

Deployment patterns

  • Edge-first deployment model with server-side AI adapters and public-safe secrets handling
  • Stateless agent gateway with provider abstraction, retries, cost controls, and trace capture

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: tool calling, retrieval, eval gates, prompt/version control, cost accounting, and deterministic fallback paths

Engineering patterns

  • Treat tool calls as typed contracts with retries, timeouts, validation, and trace identifiers
  • Evaluate agent behavior with replay fixtures, golden traces, and cost/latency accounting
  • 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

  • prompt drift
  • tool-call ambiguity
  • provider outage
  • unbounded latency or spend

Attached Architecture References

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.

Revenue Architecture Overlay

See Revenue Architecture for the free-tier-first launch stack, productized offer, metering hooks, paywall boundary, and cost guardrails that turn this system architecture into a service path.