Architecture | Enterprise LLM Adoption Kit

System Architecture - enterprise-llm-adoption-kit

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	`enterprise-llm-adoption-kit`
Primary domain	agent runtime reliability and AI workflow orchestration
Primary stack	Python service or lab runtime, Terraform infrastructure modules, Container build surface, Local compose environment, 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 Python service or lab runtime, Terraform infrastructure modules, Container build surface.

Runtime And Data Flow

flowchart LR
    User["User or reviewer"] --> Surface["Public demo, CLI, package, or README surface"]
    Surface --> Runtime["Runtime boundary: Python service or lab runtime, Terraform infrastructure modules, Container build surface, Local compose environment, 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: 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	Python service or lab runtime, Terraform infrastructure modules, Container build surface, Local compose environment, 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

Infrastructure-as-code entrypoint with explicit variables, outputs, and provider boundaries
Containerized runtime path suitable for repeatable local, staging, or managed service deployment
Edge-first deployment model with server-side AI adapters and public-safe secrets handling
Stateless agent gateway with provider abstraction, retries, usage 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
usage and quota guardrails for hosted services and model adapters

AI And Automation Boundary

Operating model: tool calling, retrieval, eval gates, prompt/version control, usage 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 usage/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.