Engineering Best Practices

// Zod schema — validate & sanitize at every boundary
const CreateUserSchema = z.object({
  email: z.string().email().max(255),
  name:  z.string().min(1).max(100).regex(/^[\w\s'-]+$/),
  role:  z.enum(['user', 'admin']).default('user'),
});

app.post('/users', async (req, res) => {
  const parsed = CreateUserSchema.safeParse(req.body);
  if (!parsed.success) {
    return res.status(400).json({ errors: parsed.error.format() });
  }
  // parsed.data is now type-safe & sanitized — never trust raw req.body
  await userService.create(parsed.data);
});

// Clean Architecture: dependencies always point inward

// Domain layer — zero external dependencies
interface UserRepository {
  findById(id: string): Promise<User | null>;
  save(user: User): Promise<void>;
}

// Application layer — orchestrates domain objects
class CreateUserUseCase {
  constructor(private readonly users: UserRepository) {}
  async execute(cmd: CreateUserCommand): Promise<string> {
    const user = User.create(cmd.name, cmd.email);
    await this.users.save(user);
    return user.id;
  }
}

// Infrastructure layer — implements the port
class PrismaUserRepository implements UserRepository { ... }

# LangGraph: typed state machine for reliable agents
from langgraph.graph import StateGraph, END
from typing import TypedDict, Annotated
import operator

class AgentState(TypedDict):
    messages: Annotated[list, operator.add]
    tool_calls: list

builder = StateGraph(AgentState)
builder.add_node("reason", reasoning_node)
builder.add_node("act",    tool_execution_node)
builder.add_conditional_edges(
    "reason",
    lambda s: "act" if s["tool_calls"] else END
)
builder.add_edge("act", "reason")  # loop until done
graph = builder.compile(checkpointer=memory_saver)

# OpenAPI 3.1 — cursor pagination + rate-limit headers
paths:
  /api/v1/items:
    get:
      operationId: listItems
      security: [{ bearerAuth: [] }]
      parameters:
        - { name: cursor, in: query, schema: { type: string } }
        - { name: limit,  in: query, schema: { type: integer,
            minimum: 1, maximum: 100, default: 20 } }
      responses:
        '200':
          headers:
            X-RateLimit-Remaining:
              schema: { type: integer }
          content:
            application/json:
              schema: { $ref: '#/components/schemas/ItemPage' }
        '429':
          description: Rate limit exceeded
          headers:
            Retry-After: { schema: { type: integer } }

# 7-stage DevSecOps pipeline (GitHub Actions)
jobs:
  sast:
    steps:
      - uses: github/codeql-action/analyze@v3
      - run: semgrep --config=auto --sarif -o semgrep.sarif
  deps-audit:
    steps:
      - run: npm audit --audit-level=high
      - uses: snyk/actions/node@master
  container-scan:
    steps:
      - uses: aquasecurity/trivy-action@master
        with:
          image-ref: '${{ env.IMAGE_TAG }}'
          severity: 'CRITICAL,HIGH'
          exit-code: '1'
  provenance:
    needs: [sast, deps-audit, container-scan]
    uses: slsa-framework/slsa-github-generator/.github/workflows/generator_generic_slsa3.yml@v2

# Typed agent tool with validated inputs (LangChain)
from langchain.tools import BaseTool
from pydantic import BaseModel, Field

class SearchInput(BaseModel):
    query: str = Field(description="Semantic search query")
    k: int     = Field(default=5, ge=1, le=20)

class VectorSearchTool(BaseTool):
    name        = "vector_search"
    description = "Semantic search over knowledge base"
    args_schema = SearchInput

    def _run(self, query: str, k: int = 5) -> list[dict]:
        results = self.retriever.invoke(query, k=k)
        return [{"content": r.page_content,
                 "score":   r.metadata.get("score", 0)}
                for r in results]

// Hexagonal Architecture: ports define the domain boundary
// Port lives in domain — no framework imports

interface OrderRepository {           // port
  findById(id: OrderId): Promise<Order | null>;
  save(order: Order): Promise<void>;
}

// Adapter lives in infrastructure
class PostgresOrderRepository implements OrderRepository {
  async findById(id: OrderId) {
    const row = await db.query(
      'SELECT * FROM orders WHERE id = $1', [id.value]
    );
    return row ? OrderMapper.toDomain(row) : null;
  }
}

// Application layer command — no DB dependency
class PlaceOrderHandler {
  constructor(private readonly orders: OrderRepository) {}
  async handle(cmd: PlaceOrderCommand): Promise<void> {
    const order = Order.place(cmd.items, cmd.userId);
    await this.orders.save(order);
  }
}

# Qdrant: hybrid vector + metadata filtered search
from qdrant_client import QdrantClient
from qdrant_client.models import Filter, FieldCondition, Range

client = QdrantClient(url="http://localhost:6333")

results = client.search(
    collection_name="knowledge_base",
    query_vector=embedder.embed("API design best practices"),
    query_filter=Filter(
        must=[FieldCondition(
            key="metadata.trust_score",
            range=Range(gte=0.7)
        )]
    ),
    limit=10,
    with_payload=True,
)
# results[0].payload["content"], results[0].score

# ArgoCD Application — fully declarative GitOps deployment
apiVersion: argoproj.io/v1alpha1
kind: Application
metadata:
  name: structured-for-growth
  namespace: argocd
spec:
  project: default
  source:
    repoURL: https://github.com/org/k8s-manifests
    targetRevision: main
    path: apps/structured-for-growth
  destination:
    server: https://kubernetes.default.svc
    namespace: production
  syncPolicy:
    automated:
      prune: true
      selfHeal: true
    syncOptions:
      - CreateNamespace=true
      - ServerSideApply=true

// React Native: biometric auth before secure storage access
import * as SecureStore       from 'expo-secure-store';
import * as LocalAuthentication from 'expo-local-authentication';

async function getTokenSecurely(key: string): Promise<string | null> {
  const auth = await LocalAuthentication.authenticateAsync({
    promptMessage:          'Authenticate to continue',
    disableDeviceFallback:  false,
    cancelLabel:            'Cancel',
  });
  if (!auth.success) throw new Error('Biometric auth failed');

  return SecureStore.getItemAsync(key, {
    requireAuthentication:  true,
    keychainAccessible:
      SecureStore.WHEN_UNLOCKED_THIS_DEVICE_ONLY,
  });
}

// Per-request trust score (0–100) from layered signals
function computeTrustScore(req) {
  let score = 0;
  const factors = [];
  if (hasValidSession(req)) { score += 30; factors.push('session'); }
  if (knownDevice(req))     { score += 20; factors.push('device');  }
  if (expectedGeo(req))     { score += 15; factors.push('geo');     }
  score += anomalyScore(req);          // behavioural deviation
  return { score: Math.min(score, 100), factors };
}

// Micro-segmentation: deny or step-up below the route threshold
if (computeTrustScore(req).score < ROUTE_MIN_TRUST) {
  return requireStepUp(res);           // not a one-time login gate
}

# MCP tool: typed input, least-privilege, structured errors
@mcp.tool()
def query_control(framework: str, control_id: str) -> dict:
    """Read-only lookup of a single compliance control."""
    if framework not in ALLOWED_FRAMEWORKS:        # boundary validation
        return {"error": "unknown framework", "code": "E_SCOPE"}
    control = catalog.get(framework, control_id)
    if control is None:
        return {"error": "not found", "code": "E_404"}
    return {"id": control.id, "text": control.text,
            "family": control.family}              # no write path exposed

# Hybrid retrieval: fuse BM25 lexical + dense, then rerank
def retrieve(query, k=10):
    lexical = bm25.search(query, k=k * 3)          # exact-term recall
    dense   = vectors.search(embed(query), k=k * 3)  # semantic recall
    fused   = reciprocal_rank_fusion(lexical, dense)
    ranked  = trust_score(fused)                   # reliability of source
    return ranked[:k]                              # grounded, cited context

# FrugalGPT-style cascade: escalate only when cheap output fails
def route(prompt):
    load = score_cognitive_load(prompt)    # 1–10: heuristic or ~$0.001 Haiku
    for model in TIERS[tier_for(load):]:   # cheapest viable model first
        answer = model.complete(compress(prompt))
        if passes_quality_gate(answer):
            return answer                  # stop at first good-enough tier
    return answer                          # top tier is the fallback