ai-app-architecture

name	ai-app-architecture
description	Use when designing or building AI-powered application systems — choosing architecture style, selecting components, structuring the AI stack, making build-vs-buy decisions, and planning multi-tenant AI module gating
metadata	{"portable":true,"compatible_with":["claude-code","codex"]}

AI Application Architecture

Acknowledgement: Shared by Peter Bamuhigire, techguypeter.com, [phone].

Use When

• Use when designing or building AI-powered application systems — choosing architecture style, selecting components, structuring the AI stack, making build-vs-buy decisions, and planning multi-tenant AI module gating
• The task needs reusable judgment, domain constraints, or a proven workflow rather than ad hoc advice.

Do Not Use When

• The task is unrelated to ai-app-architecture or would be better handled by a more specific companion skill.
• The request only needs a trivial answer and none of this skill's constraints or references materially help.

Required Inputs

• Gather relevant project context, constraints, and the concrete problem to solve.
• Confirm the desired deliverable: design, code, review, migration plan, audit, or documentation.

Workflow

• Read this SKILL.md first, then load only the referenced deep-dive files that are necessary for the task.
• Apply the ordered guidance, checklists, and decision rules in this skill instead of cherry-picking isolated snippets.
• Produce the deliverable with assumptions, risks, and follow-up work made explicit when they matter.

Quality Standards

• Keep outputs execution-oriented, concise, and aligned with the repository's baseline engineering standards.
• Preserve compatibility with existing project conventions unless the skill explicitly requires a stronger standard.
• Prefer deterministic, reviewable steps over vague advice or tool-specific magic.

Anti-Patterns

• Treating examples as copy-paste truth without checking fit, constraints, or failure modes.
• Loading every reference file by default instead of using progressive disclosure.

Outputs

• A concrete result that fits the task: implementation guidance, review findings, architecture decisions, templates, or generated artifacts.
• Clear assumptions, tradeoffs, or unresolved gaps when the task cannot be completed from available context alone.
• References used, companion skills, or follow-up actions when they materially improve execution.

Evidence Produced

Category	Artifact	Format	Example
Correctness	AI architecture decision record	Markdown doc per skill-composition-standards/references/adr-template.md covering provider, deployment, and integration choices	docs/ai/architecture-adr-assistant.md

References

• Use the links and companion skills already referenced in this file when deeper context is needed.
• references/practical-ai-engineering.md - evaluation-first AI engineering, RAG quality checks, agentic workflow controls, guardrails, telemetry, and cost discipline.
• references/ai-transformation-operating-model.md - operating-model, adoption, governance, human-centred design, and sustainability gates distilled from supplied AI transformation source material.

Overview

AI-powered apps are built on top of foundation models via APIs. You are NOT training models — you are orchestrating them. Your value lies in the application layer: context construction, prompt engineering, retrieval, guardrails, and user experience.

Core principle: Start with the simplest architecture that works. Evolve deliberately.

Premier Agency Standard

Design AI systems for measurable economic value, production reliability, and long-term maintainability. Every serious AI application must connect model behavior to a business workflow, revenue lever, cost reduction, risk reduction, or service-quality improvement.

Before selecting a model or framework, define:

• Business outcome: the operational metric the AI feature should improve.
• User decision/action: what the user or system will do differently because AI exists.
• Data advantage: which proprietary, local, domain, or workflow data improves the result.
• Failure cost: what happens when the model is wrong, slow, unavailable, biased, or expensive.
• Evaluation target: the minimum acceptable quality, latency, cost, and safety threshold.
• Operating owner: who monitors, tunes, approves, and maintains the feature after launch.

Reject AI features that cannot state their economic value or decision impact.

---

Architecture Styles (Choose One to Start)

Style	Description	When to Use
Wrap	Your UI + prompt engineering wraps a commercial LLM API	First project, internal tools, quick wins
RAG	Retriever fetches private/fresh data, injected into prompt	Apps needing company-specific or up-to-date knowledge
Workflow	Deterministic steps call models only where judgment or language is needed	Business processes with predictable stages and audit requirements
Agentic	LLM plans and executes multi-step tasks using tools	Complex automation, multi-step workflows
Fine-tuned	Model weights adapted for domain/style	Only when brand voice or jargon cannot be achieved via prompts

Default path: Wrap -> RAG -> deterministic workflow -> agents. Only fine-tune when prompts, retrieval, examples, routing, and workflow design cannot meet the target.

---

The AI Application Stack

┌──────────────────────────────────┐
│   User Interface (web/mobile)    │
├──────────────────────────────────┤
│   Input Guardrail                │  ← block PII, prompt injection, off-topic
│   Router / Intent Classifier     │  ← route to right model/solution
│   Context Builder (RAG / Tools)  │  ← feature engineering for AI
│   Model Gateway                  │  ← unified API wrapper, key mgmt, fallbacks
│   LLM API (OpenAI/Codex/Gemini) │
│   Output Guardrail               │  ← catch toxicity, format failures, PII
│   Cache Layer                    │  ← exact + semantic caching
│   Streaming Handler              │  ← MANDATORY — never block on full generation
├──────────────────────────────────┤
│   Token Ledger (MANDATORY)       │  ← log every call: tenant_id, user_id, tokens
│   AI Module Gate (OFF by default)│  ← per-tenant enable/disable
└──────────────────────────────────┘

---

Component Responsibilities

Input Guardrail

• Detect and mask PII before sending to external APIs
• Block prompt injection patterns
• Enforce topic restrictions (domain scope)
• Tools: Meta Purple Llama, NVIDIA NeMo Guardrails, OpenAI Moderation API

Router

• Classify intent → route to right model or solution
• Send simple queries to cheaper models (BERT, GPT-mini)
• Detect out-of-scope queries before wasting API calls
• Detect ambiguous queries → ask for clarification
• Pattern: routing → retrieval → generation → scoring

Context Builder

• Context construction = feature engineering for AI
• Retrieve relevant chunks (RAG), live data (APIs), user profile
• This is where most quality improvement happens — invest here
• Version prompts, retrieval settings, chunking rules, schemas, and tool definitions as production configuration
• Separate tenant/user context from global knowledge to prevent cross-client data leakage

Model Gateway

• Centralises all LLM provider calls (OpenAI, Anthropic, Google, self-hosted)
• Centralises: API key management, rate limiting, logging, fallback policies
• Enables swapping providers without touching application code
• Tools: Portkey AI Gateway, MLflow AI Gateway, Kong
• Expose common controls: request id, tenant id, model id, prompt version, timeout, retry policy, budget class, and safety profile

Output Guardrail

• Catch format failures (invalid JSON/schema) → retry automatically
• Catch hallucinations, toxic content, brand-risk responses
• Fall back to human operators for sensitive/tricky queries
• Note: streaming mode complicates output guardrails — plan for partial responses

Semantic Cache

• Exact cache: identical queries → return stored response
• Semantic cache: similar queries → return stored response (use embedding similarity)
• Cache at vector search level too (expensive embedding calls)
• Warning: improper cache can leak user-specific data — use tenant-scoped cache keys

---

Architecture Evolution Pattern

Step 1 (Baseline):   Query → Model API → Response
Step 2 (Context):    Query → Retriever → [Context + Query] → Model → Response
Step 3 (Guardrails): Input Guard → Context → Model → Output Guard → Response
Step 4 (Router):     Router → [Intent-specific path] → Model(s) → Response
Step 5 (Cache):      Router → Cache → [miss: full pipeline] → Cache Store
Step 6 (Agents):     Router → Agent Loop [Plan → Tools → Reflect] → Response

Add each layer only when its absence is causing a real problem.

For production AI features, load references/practical-ai-engineering.md before finalising the architecture. It adds evaluation, RAG, agent, safety, fallback, telemetry, and cost-control gates.

Production AI Platform Requirements

Capability	Minimum Standard
Versioning	Version prompts, models, tools, retrieval indexes, evaluation datasets, and safety policies
Observability	Log quality signals, cost, latency, failures, tool calls, cache hits, and user feedback by tenant and feature
Evaluation	Maintain golden sets, regression tests, adversarial cases, and release thresholds before launch
Governance	Document data flow, retention, PII handling, model/provider choice, human approval points, and audit trail
Resilience	Add timeouts, retries with backoff, fallbacks, degraded UX, queueing for long work, and circuit breakers
Data quality	Treat data pipelines, embeddings, metadata, and retrieval filters as first-class production assets
Explainability	Provide citations, source snippets, confidence bands, or reasoning summaries where users must trust decisions
Maintenance	Assign owners for prompt updates, eval refreshes, model migrations, cost reviews, and incident response

Workflow vs Agent Decision

• Use a deterministic workflow when the process steps are known, regulated, auditable, or cost-sensitive.
• Use an agent when the path genuinely depends on intermediate observations, tool results, or open-ended planning.
• Keep agents on a short leash: bounded tool set, max steps, scoped memory, explicit approvals, and rollback-safe actions.
• Do not use agents for simple summarization, extraction, classification, or transformation.

Data and ML System Design Checks

• Define online/offline data sources, freshness requirements, ownership, quality checks, and missing-data behavior.
• Choose metrics that reflect the business objective, not only model accuracy.
• Plan for distribution shift: seasonality, new user behavior, changing regulations, platform algorithm changes, and language mix.
• Store enough inputs, outputs, versions, and feedback to debug regressions without storing unnecessary sensitive data.
• Separate training/evaluation data from production data where supervised learning or fine-tuning is used.

---

Build vs Buy Decision

Option	Effort	Control	Cost	When
Commercial API (OpenAI/Codex)	Low	Low	Per-token	Default choice
Open source self-hosted (Llama)	High	Full	GPU infra	Data privacy requirement, high volume
Fine-tuned commercial	Medium	Partial	Training + inference	Brand voice, jargon control
Fine-tuned self-hosted	Very High	Full	High	Maximum control, regulated industries

Rule: API wrap first. Justify self-hosting with actual cost/compliance numbers.

---

AI Module Gating (MANDATORY in SaaS)

Every AI feature MUST be gated. AI costs real money per token.

-- Schema: AI module per tenant
CREATE TABLE tenant_ai_config (
  tenant_id     INT PRIMARY KEY,
  ai_enabled    BOOLEAN DEFAULT FALSE,   -- OFF by default
  monthly_budget_usd DECIMAL(10,2),      -- null = unlimited
  budget_alert_pct   INT DEFAULT 80,     -- alert at 80% of budget
  plan_name     VARCHAR(50),             -- 'basic', 'pro', 'enterprise'
  enabled_at    TIMESTAMP,
  created_at    TIMESTAMP DEFAULT NOW()
);

Enforcement: Every AI endpoint checks tenant_ai_config.ai_enabled before processing. Return 402 Payment Required if disabled.

---

Token Ledger (MANDATORY)

Log every AI API call for billing, debugging, and cost visibility.

CREATE TABLE ai_token_usage (
  id            BIGINT AUTO_INCREMENT PRIMARY KEY,
  tenant_id     INT NOT NULL,
  user_id       INT NOT NULL,
  feature_name  VARCHAR(100),            -- 'invoice_analysis', 'report_summary'
  model         VARCHAR(50),             -- 'gpt-4o', 'Codex-3-sonnet'
  tokens_in     INT NOT NULL,
  tokens_out    INT NOT NULL,
  cost_usd      DECIMAL(10,6),           -- calculated at log time
  latency_ms    INT,
  created_at    TIMESTAMP DEFAULT NOW(),
  INDEX idx_tenant_date (tenant_id, created_at),
  INDEX idx_user_date (user_id, created_at)
);

-- Usage by tenant (for invoicing)
SELECT tenant_id,
       SUM(tokens_in + tokens_out) AS total_tokens,
       SUM(cost_usd) AS total_cost_usd,
       DATE_FORMAT(created_at, '%Y-%m') AS month
FROM ai_token_usage
GROUP BY tenant_id, month;

-- Usage by user (for analytics)
SELECT user_id, feature_name,
       SUM(tokens_in + tokens_out) AS tokens,
       COUNT(*) AS calls
FROM ai_token_usage
WHERE tenant_id = ? AND created_at > DATE_SUB(NOW(), INTERVAL 30 DAY)
GROUP BY user_id, feature_name;

---

Quota Enforcement

function checkAiQuota(int $tenantId): void {
    $config = TenantAiConfig::find($tenantId);

    if (!$config || !$config->ai_enabled) {
        throw new AiModuleDisabledException('AI module not enabled for this account.');
    }

    if ($config->monthly_budget_usd !== null) {
        $spent = AiTokenUsage::currentMonthCost($tenantId);
        if ($spent >= $config->monthly_budget_usd) {
            throw new AiBudgetExceededException('Monthly AI budget reached.');
        }
        if ($spent >= $config->monthly_budget_usd * ($config->budget_alert_pct / 100)) {
            notifyTenantBudgetAlert($tenantId, $spent, $config->monthly_budget_usd);
        }
    }
}

---

Infrastructure Options

Layer	Lightweight	Production
LLM	OpenAI API	API + fallback provider via gateway
Context	In-memory / SQLite	Vector DB (Chroma, Qdrant, Pinecone)
Cache	Redis	Redis Cluster
Queue	Sync	Kafka / RabbitMQ
Monitoring	Log file	Prometheus + Grafana

---

Anti-Patterns

• No module gating — every user can trigger AI calls, destroying your margins
• No token logging — you cannot invoice clients or debug runaway costs
• Orchestrator too early — LangChain/LlamaIndex before you understand your pipeline adds complexity
• Fine-tuning first — always try prompt engineering and RAG before fine-tuning
• Blocking on full generation — always stream tokens to the user immediately
• Hard-coded system prompts — make prompts configurable, not hardcoded in code

---

Sources

Chip Huyen — AI Engineering (2025); David Spuler — Generative AI Applications (2024); Andrea De Mauro — AI Applications Made Easy (2024)

Consolidated Child References

• Load references/routing.md to map retired AI child skill slugs to their reference modules.