A comprehensive technical guide to understanding, evaluating, and implementing agentic AI systems in enterprise environments. Learn the architecture, capabilities, and real-world applications of autonomous AI agents.

Enterprise Guide to Agentic AI Systems

A hands-on technical guide to orchestrating multiple AI models in production. Learn request routing, model selection, fallback strategies, and load balancing patterns that actually work.

The Complete Guide to AI Orchestration

## Our Philosophy on AI

Let me be direct with you: most AI implementations fail. Not because the technology doesn't work, but because companies rush into AI without understanding what they're building or why.

We've seen it repeatedly. A company wants "AI" because competitors have it. They sign up for some SaaS platform, plug in an API, and call it done. Six months later, they're locked into a vendor, paying escalating fees, and their "AI solution" is just a glorified chatbot that frustrates customers.

That's not how we work.

Our philosophy is simple: **you own your AI**. Not in some abstract sense, but literally. The models, the data, the infrastructure, the code. When we build AI systems, you can take everything and run it yourself tomorrow if you want.

> AI should be a capability you own, not a subscription you rent.

This matters more than most people realize. AI is becoming core infrastructure. Handing that to a vendor is like outsourcing your entire engineering team. Fine for experiments, dangerous for production.

## What We Mean by AI Systems

When we talk about AI systems, we're not talking about chatbots. We're talking about intelligent software that actually does work.

| AI Type | What It Does | Example |
|---------|--------------|---------|
| **Conversational AI** | Handles natural language interactions | Customer support, internal assistants |
| **Agentic Systems** | Performs multi-step tasks autonomously | Research, document processing, workflow automation |
| **RAG Systems** | Retrieves and reasons over your data | Knowledge bases, document Q&A, compliance checking |
| **Classification** | Categorizes and routes information | Ticket routing, content moderation, lead scoring |
| **Extraction** | Pulls structured data from unstructured sources | Invoice processing, contract analysis, data entry |
| **Generation** | Creates content following your guidelines | Reports, summaries, drafts, translations |

Most projects combine several of these. A customer support system might use conversational AI for the interface, RAG for accessing product knowledge, classification for routing, and generation for drafting responses.

## The Architecture We Build

Let me walk you through our typical AI architecture. This isn't theoretical—it's what runs in production for our clients.

### Layer 1: Model Orchestration

At the top sits the orchestration layer. This is the brain that decides which models to use, when to use them, and how to combine their outputs.

```typescript
interface ModelOrchestrator {
  // Route requests to appropriate models
  route(request: AIRequest): ModelSelection;

  // Execute with fallback chain
  execute(request: AIRequest): Promise<AIResponse>;

  // Aggregate multi-model responses
  aggregate(responses: AIResponse[]): AIResponse;
}

// Example: Route based on task complexity
const router = {
  route(request) {
    if (request.requiresReasoning) {
      return { primary: 'claude-opus', fallback: 'gpt-4o' };
    }
    if (request.isSimpleQuery) {
      return { primary: 'claude-haiku', fallback: 'gpt-4o-mini' };
    }
    return { primary: 'claude-sonnet', fallback: 'gpt-4o' };
  }
};
```

Why orchestration matters: you're not locked to one model. When a better model comes out, you switch. When prices change, you optimize. When one provider has an outage, you failover.

### Layer 2: Context Management

AI is only as good as the context you give it. This layer handles everything related to building that context.

**Short-term Context**: The current conversation, recent actions, immediate task state.

**Long-term Memory**: What the system has learned over time. User preferences, past interactions, accumulated knowledge.

**Retrieved Context**: Information pulled from your knowledge bases, databases, and documents via RAG.

```typescript
async function buildContext(request: AIRequest): Promise<Context> {
  const [shortTerm, longTerm, retrieved] = await Promise.all([
    getConversationHistory(request.sessionId),
    getUserMemory(request.userId),
    retrieveRelevantDocs(request.query)
  ]);

  return {
    systemPrompt: buildSystemPrompt(request.task),
    conversation: shortTerm,
    memory: longTerm,
    documents: retrieved,
    metadata: {
      timestamp: Date.now(),
      user: request.userId,
      session: request.sessionId
    }
  };
}
```

### Layer 3: Tool Integration

AI systems need to do things, not just talk. This layer connects AI to your actual business systems.

| Tool Category | Examples | Why It Matters |
|---------------|----------|----------------|
| **Data Access** | Database queries, API calls, file reads | AI can answer questions about real data |
| **Actions** | Send emails, create tickets, update records | AI can actually complete tasks |
| **Computation** | Run calculations, execute code, generate reports | AI can handle complex analysis |
| **External Services** | Search, payments, shipping, calendars | AI can interact with the outside world |

The key is sandboxing. Every tool has explicit permissions. The AI can only do what you've authorized, nothing more.

```typescript
const toolPermissions = {
  customerSupport: {
    canRead: ['orders', 'products', 'tickets'],
    canWrite: ['tickets', 'notes'],
    cannotAccess: ['payments', 'internal_docs']
  },
  salesAgent: {
    canRead: ['products', 'pricing', 'leads'],
    canWrite: ['leads', 'quotes'],
    cannotAccess: ['customer_data', 'financials']
  }
};
```

### Layer 4: Evaluation & Monitoring

You can't improve what you can't measure. This layer tracks everything.

- **Quality Metrics**: Are responses accurate? Helpful? On-brand?
- **Performance Metrics**: Latency, throughput, cost per request
- **Safety Metrics**: Are guardrails working? Any policy violations?
- **Business Metrics**: Resolution rate, customer satisfaction, time saved

```typescript
interface AIMetrics {
  // Quality
  accuracy: number;        // Via human evaluation or automated checks
  relevance: number;       // Did we answer the actual question?
  coherence: number;       // Does the response make sense?

  // Performance
  latencyP50: number;
  latencyP99: number;
  tokensUsed: number;
  costUSD: number;

  // Safety
  guardrailTriggered: boolean;
  policyViolation: boolean;
  escalatedToHuman: boolean;
}
```

## RAG: Making AI Know Your Business

Retrieval-Augmented Generation is how we make AI actually useful for your specific business. Instead of relying on general knowledge, the AI retrieves information from your documents, databases, and knowledge bases before responding.

### How RAG Works

```
1. User asks: "What's our return policy for enterprise customers?"

2. System searches your knowledge base for relevant documents

3. Retrieves: Enterprise Agreement v2.3, Returns Policy, Support SLA

4. AI reads these documents and generates an accurate answer

5. Response includes citations so users can verify
```

### Building Effective RAG

The difference between good RAG and bad RAG is massive. Here's what we've learned:

**Chunking Strategy Matters**

Don't just split documents at arbitrary character limits. Split semantically—by section, paragraph, or logical unit.

```typescript
// Bad: Fixed-size chunks break context
const badChunks = splitByCharacters(document, 1000);

// Good: Semantic chunking preserves meaning
const goodChunks = splitBySections(document, {
  respectHeadings: true,
  keepParagraphsIntact: true,
  maxTokens: 512,
  overlapTokens: 50
});
```

**Hybrid Search Wins**

Pure vector search misses exact matches. Pure keyword search misses semantic similarity. Use both.

| Search Type | Good For | Bad For |
|-------------|----------|---------|
| **Vector/Semantic** | Conceptual questions, paraphrasing | Exact names, numbers, codes |
| **Keyword/BM25** | Specific terms, product names, IDs | Conceptual queries, synonyms |
| **Hybrid** | Everything | Slightly more complex to implement |

**Reranking Improves Quality**

First-pass retrieval gets candidates. Reranking sorts them by actual relevance to the query.

```typescript
async function retrieveWithRerank(query: string): Promise<Document[]> {
  // Get more candidates than needed
  const candidates = await hybridSearch(query, { limit: 20 });

  // Rerank to find the best
  const reranked = await reranker.rank(query, candidates);

  // Return top results
  return reranked.slice(0, 5);
}
```

## Model Selection: No Single Answer

There's no "best" AI model. There's the best model for your specific task, budget, and requirements.

| Model | Best For | Trade-offs |
|-------|----------|------------|
| **Claude Opus** | Complex reasoning, nuanced tasks | Higher cost, slower |
| **Claude Sonnet** | General-purpose, good balance | Middle ground on everything |
| **Claude Haiku** | High-volume, simple tasks | Less capable on complex work |
| **GPT-4o** | Strong alternative, good ecosystem | Different strengths/weaknesses |
| **GPT-4o-mini** | Cost-sensitive applications | Less capable than full models |
| **Open Source (Llama, Mistral)** | Privacy, cost control, customization | More operational overhead |

Our approach: design for model flexibility. Today's best model won't be tomorrow's. Your architecture should make switching easy.

## Ownership & No Lock-in

Let me explain what we mean by ownership in concrete terms.

**You Own the Code**

Everything we build for you is yours. Not licensed to you, not accessible through our platform—actually yours. Git repository, full history, all documentation.

**You Own the Data**

Your training data, your embeddings, your vector databases, your conversation logs. All of it stays in your infrastructure or gets handed over at project end.

**You Own the Models**

When we fine-tune models for you, those fine-tuned weights are yours. You can run them on your own infrastructure.

**No Proprietary Dependencies**

We don't build systems that require our proprietary tools to run. Everything uses open standards, open-source tools, and documented APIs.

```
What you get at project end:
├── Source Code (full repository)
├── Documentation
│   ├── Architecture docs
│   ├── API documentation
│   ├── Operational runbooks
│   └── Training materials
├── Data
│   ├── Training datasets
│   ├── Vector embeddings
│   └── Evaluation sets
├── Infrastructure
│   ├── Terraform/Pulumi configs
│   ├── Kubernetes manifests
│   └── CI/CD pipelines
└── Models
    ├── Fine-tuned weights
    ├── Prompt libraries
    └── Evaluation benchmarks
```

## Where AI Actually Works Today

Let's be honest about what AI can and can't do reliably in production.

### High Confidence Applications

| Use Case | Why It Works | Example Impact |
|----------|--------------|----------------|
| **Customer Support Tier-1** | Well-defined scope, easy to verify | 40-60% automation rate |
| **Document Q&A** | RAG makes it accurate, citations verify | Hours→minutes for research |
| **Content Drafting** | Human reviews before publish | 3x faster content production |
| **Code Assistance** | Developer validates output | 20-30% productivity gain |
| **Data Extraction** | Structured output, easy to check | 90% reduction in manual entry |

### Proceed with Caution

| Use Case | Challenge | Mitigation |
|----------|-----------|------------|
| **Medical/Legal Advice** | Liability, accuracy critical | Always human review |
| **Financial Decisions** | Hallucination risk, high stakes | Guardrails + human approval |
| **Autonomous Actions** | Hard to undo mistakes | Start with suggestions only |
| **Creative with Brand Risk** | Off-brand outputs | Style guides + review workflow |

### Not Ready Yet

- Fully autonomous customer-facing agents without fallbacks
- High-stakes decisions without human oversight
- Anything requiring perfect accuracy 100% of the time

## Getting Started with Us

Here's how we typically engage on AI projects:

**Phase 1: Discovery (1-2 weeks)**
We understand your business, your data, your existing systems. We identify where AI can actually help versus where it's just hype.

**Phase 2: Proof of Concept (4-6 weeks)**
We build a working prototype on real data. You see actual results, not slide decks. We measure performance and validate the approach.

**Phase 3: Production Build (8-16 weeks)**
Full implementation with all the layers described above. Proper monitoring, security, and operational tooling.

**Phase 4: Handoff & Support**
You own everything. We train your team. We provide support as needed, but you're never dependent on us.

## Conclusion

AI is transformative technology, but only if you do it right. That means:

- Building for ownership, not dependency
- Starting with clear use cases, not vague "AI strategy"
- Measuring everything and iterating based on data
- Keeping humans in the loop for high-stakes decisions
- Designing for model flexibility from day one

> The companies that will win with AI aren't those who adopt it first. They're those who adopt it correctly—with systems they own, understand, and can evolve.

We've helped organizations across industries build AI systems that actually work. Not demos that impress in meetings, but production systems that deliver measurable business value.

If you're thinking about AI for your organization, we'd be happy to share what we've learned.

A comprehensive guide to building enterprise AI systems with full ownership and control. Learn about agentic architecture, RAG systems, model selection, and how to avoid vendor lock-in while leveraging cutting-edge AI capabilities.

AI Systems & Agentic Architecture

## Why Most AI Workflows Fail in Production

Here's something we've learned the hard way: getting an AI to do something impressive in a demo is easy. Getting it to do that same thing reliably, thousands of times a day, with real data and real edge cases? That's where most teams hit a wall.

We've built AI workflows for document processing, customer support automation, content generation, and data analysis. Along the way, we've made every mistake you can imagine. This guide is what we wish someone had told us before we started.

> The difference between a prototype and a production system isn't the model you use. It's everything around the model.

Let me walk you through how we actually design AI workflows now, after learning these lessons.

## The Anatomy of a Production AI Pipeline

Think of an AI workflow as a series of processing stages, each with a specific job. Here's the basic structure we use:

```
Input → Validation → Preprocessing → AI Processing → Post-processing → Validation → Output
         ↓              ↓               ↓                ↓              ↓
      [Error]       [Transform]     [LLM Call]       [Parse]       [Quality]
      Handler       + Enrich         + Tools         + Format       Check
```

Each stage has clear inputs, outputs, and failure modes. Let's break them down.

### Stage 1: Input Validation and Normalization

Never trust incoming data. Ever. We learned this when a customer sent us a "text document" that was actually a 50MB binary file. The pipeline choked, the queue backed up, and we spent a weekend fixing it.

```javascript
const validateInput = async (input) => {
  const checks = {
    exists: input !== null && input !== undefined,
    sizeOk: Buffer.byteLength(input, 'utf8') < MAX_INPUT_SIZE,
    formatOk: isValidFormat(input),
    contentOk: !containsMaliciousPatterns(input)
  };

  const failures = Object.entries(checks)
    .filter(([_, passed]) => !passed)
    .map(([check]) => check);

  if (failures.length > 0) {
    throw new ValidationError(`Input failed: ${failures.join(', ')}`);
  }

  return normalizeInput(input);
};
```

**What to check:**
- File size limits (set them lower than you think you need)
- Format validation (is it actually JSON, not just named `.json`?)
- Character encoding (UTF-8 issues will haunt you)
- Content safety (don't send malicious content to your LLM)

### Stage 2: Preprocessing and Enrichment

Raw input rarely goes straight to an LLM. You usually need to transform it, add context, or break it into chunks.

| Preprocessing Task | When to Use | Example |
|-------------------|-------------|---------|
| Chunking | Long documents exceeding context limits | Split a 100-page PDF into 2000-token chunks |
| Enrichment | Need additional context | Add customer history before processing support ticket |
| Extraction | Only parts of input are relevant | Pull just the "description" field from a JSON payload |
| Transformation | Format conversion needed | Convert HTML to markdown for cleaner processing |
| Deduplication | Repeated content wastes tokens | Remove duplicate paragraphs from scraped content |

Here's a chunking strategy we actually use:

```javascript
const chunkDocument = (text, options = {}) => {
  const {
    maxTokens = 2000,
    overlap = 200,
    preserveParagraphs = true
  } = options;

  const chunks = [];
  let currentChunk = '';

  const paragraphs = text.split(/\n\n+/);

  for (const para of paragraphs) {
    const combined = currentChunk + '\n\n' + para;

    if (estimateTokens(combined) > maxTokens) {
      if (currentChunk) {
        chunks.push(currentChunk.trim());
        // Keep overlap for context continuity
        currentChunk = getLastNTokens(currentChunk, overlap) + '\n\n' + para;
      } else {
        // Single paragraph too long, force split
        chunks.push(...forceSplitParagraph(para, maxTokens));
        currentChunk = '';
      }
    } else {
      currentChunk = combined;
    }
  }

  if (currentChunk.trim()) {
    chunks.push(currentChunk.trim());
  }

  return chunks;
};
```

The overlap is crucial. Without it, information that spans chunk boundaries gets lost. We learned this when our document summarizer kept missing key points that happened to fall between chunks.

## Designing the AI Processing Core

This is where the actual LLM calls happen. But a single call is rarely enough for complex tasks.

### The Prompt Chain Pattern

Instead of one giant prompt trying to do everything, break complex tasks into focused steps:

```
[Understand] → [Plan] → [Execute] → [Verify] → [Format]
```

**Example: Processing a customer complaint**

```javascript
const processComplaint = async (complaint) => {
  // Step 1: Understand - Extract key information
  const analysis = await llm.call({
    system: `Extract structured information from customer complaints.
             Return JSON with: issue_type, urgency, customer_emotion, key_details`,
    user: complaint
  });

  // Step 2: Plan - Determine response strategy
  const strategy = await llm.call({
    system: `Based on this complaint analysis, determine the response strategy.
             Consider: resolution options, escalation needs, compensation eligibility`,
    user: JSON.stringify(analysis)
  });

  // Step 3: Execute - Generate response
  const response = await llm.call({
    system: `Write a customer response following this strategy.
             Tone: empathetic, professional. Length: 2-3 paragraphs.`,
    user: `Strategy: ${JSON.stringify(strategy)}\nOriginal complaint: ${complaint}`
  });

  // Step 4: Verify - Quality check
  const verification = await llm.call({
    system: `Review this customer response. Check for:
             - Addresses all concerns? - Appropriate tone? - Actionable next steps?
             Return: {approved: boolean, issues: string[]}`,
    user: response
  });

  if (!verification.approved) {
    return await regenerateWithFeedback(response, verification.issues);
  }

  return response;
};
```

Each step is simpler, more testable, and easier to debug. When something goes wrong, you know exactly which stage failed.

### When to Chain vs. When to Parallelize

Not everything needs to be sequential. Here's how we decide:

| Pattern | Use When | Example |
|---------|----------|---------|
| **Sequential Chain** | Each step depends on previous output | Understanding → Planning → Execution |
| **Parallel Execution** | Steps are independent | Analyzing multiple documents simultaneously |
| **Map-Reduce** | Need to process items then aggregate | Summarize each chunk, then combine summaries |
| **Conditional Branching** | Different paths for different inputs | Simple queries vs. complex analysis |

Parallel execution example:

```javascript
const analyzeDocuments = async (documents) => {
  // Process all documents in parallel
  const analyses = await Promise.all(
    documents.map(doc => analyzeDocument(doc))
  );

  // Reduce: combine into final report
  const combinedReport = await llm.call({
    system: 'Synthesize these individual analyses into a cohesive report',
    user: analyses.map((a, i) => `Document ${i + 1}:\n${a}`).join('\n\n---\n\n')
  });

  return combinedReport;
};
```

## Branching Logic: Routing to the Right Handler

Real-world inputs vary wildly. A "customer message" might be a complaint, a question, a compliment, or spam. Each needs different handling.

### Classification-Based Routing

```javascript
const routeCustomerMessage = async (message) => {
  const classification = await llm.call({
    system: `Classify this customer message into exactly one category:
             - complaint: Customer expressing dissatisfaction
             - question: Customer seeking information
             - feedback: General feedback or suggestions
             - urgent: Safety issues, legal threats, executive escalation
             - spam: Irrelevant or automated messages

             Return only the category name.`,
    user: message
  });

  const handlers = {
    complaint: handleComplaint,
    question: handleQuestion,
    feedback: handleFeedback,
    urgent: escalateToHuman,
    spam: markAsSpam
  };

  const handler = handlers[classification.toLowerCase()] || handleUnknown;
  return await handler(message);
};
```

### Confidence-Based Routing

Sometimes the AI isn't sure. Build that uncertainty into your routing:

```javascript
const routeWithConfidence = async (input) => {
  const result = await llm.call({
    system: `Analyze and route this request. Return JSON:
             {
               "category": "string",
               "confidence": 0.0-1.0,
               "reasoning": "why this category"
             }`,
    user: input
  });

  if (result.confidence < 0.7) {
    // Low confidence - get human input or use fallback
    return await handleLowConfidence(input, result);
  }

  if (result.confidence < 0.9 && isHighStakes(result.category)) {
    // Medium confidence on important decisions - verify
    return await verifyThenRoute(input, result);
  }

  // High confidence - proceed automatically
  return await routeToHandler(result.category, input);
};
```

## Error Handling: Because Things Will Break

AI workflows fail in ways traditional software doesn't. The LLM might return invalid JSON, hallucinate information, or just... not follow instructions. Plan for it.

### The Error Hierarchy

```javascript
class AIWorkflowError extends Error {
  constructor(message, stage, recoverable = true) {
    super(message);
    this.stage = stage;
    this.recoverable = recoverable;
  }
}

class ValidationError extends AIWorkflowError {
  constructor(message) {
    super(message, 'validation', true);
  }
}

class LLMError extends AIWorkflowError {
  constructor(message, type) {
    super(message, 'llm_call', type !== 'rate_limit');
    this.type = type; // 'timeout', 'rate_limit', 'invalid_response', 'refused'
  }
}

class OutputError extends AIWorkflowError {
  constructor(message) {
    super(message, 'output', true);
  }
}
```

### Retry Strategies That Work

Different failures need different retry approaches:

| Error Type | Retry Strategy | Max Retries | Backoff |
|------------|---------------|-------------|---------|
| Rate limit | Wait and retry | 5 | Exponential with jitter |
| Timeout | Retry immediately | 3 | Linear |
| Invalid response | Retry with feedback | 2 | None |
| Model refused | Rephrase and retry | 2 | None |
| Server error | Wait and retry | 3 | Exponential |

```javascript
const withRetry = async (operation, options = {}) => {
  const {
    maxRetries = 3,
    backoffMs = 1000,
    backoffMultiplier = 2,
    shouldRetry = () => true
  } = options;

  let lastError;

  for (let attempt = 0; attempt <= maxRetries; attempt++) {
    try {
      return await operation();
    } catch (error) {
      lastError = error;

      if (!shouldRetry(error) || attempt === maxRetries) {
        throw error;
      }

      const delay = backoffMs * Math.pow(backoffMultiplier, attempt);
      const jitter = Math.random() * delay * 0.1;
      await sleep(delay + jitter);
    }
  }

  throw lastError;
};

// Usage with LLM calls
const safeLLMCall = async (params) => {
  return await withRetry(
    () => llm.call(params),
    {
      maxRetries: 3,
      shouldRetry: (error) => {
        if (error.type === 'rate_limit') return true;
        if (error.type === 'timeout') return true;
        if (error.type === 'server_error') return true;
        return false;
      }
    }
  );
};
```

### Self-Healing for Invalid Responses

When the LLM returns garbage, sometimes you can fix it:

```javascript
const parseWithRecovery = async (llmResponse, expectedSchema) => {
  // First attempt: direct parse
  try {
    const parsed = JSON.parse(llmResponse);
    if (validateSchema(parsed, expectedSchema)) {
      return parsed;
    }
  } catch (e) {
    // JSON parse failed, continue to recovery
  }

  // Recovery attempt: ask LLM to fix its output
  const fixed = await llm.call({
    system: `The following response should be valid JSON matching this schema:
             ${JSON.stringify(expectedSchema)}

             Fix the response to be valid JSON. Return ONLY the fixed JSON.`,
    user: llmResponse
  });

  try {
    const parsed = JSON.parse(fixed);
    if (validateSchema(parsed, expectedSchema)) {
      return parsed;
    }
  } catch (e) {
    // Still broken
  }

  // Final fallback: extract what we can
  return extractPartialData(llmResponse, expectedSchema);
};
```

## Monitoring and Observability

You can't fix what you can't see. Here's what we track:

### Key Metrics

| Metric | Why It Matters | Alert Threshold |
|--------|---------------|-----------------|
| Latency (p50, p95, p99) | User experience, timeout risk | p95 > 10s |
| Success rate | Overall health | < 95% |
| Token usage | Cost control | > 150% of baseline |
| Retry rate | Hidden instability | > 10% |
| Classification distribution | Detect drift | Significant shift from baseline |
| Output quality scores | Catch degradation | Average < 0.8 |

### Structured Logging

Every workflow run should produce traceable logs:

```javascript
const runWorkflow = async (input, context) => {
  const runId = generateRunId();
  const startTime = Date.now();

  const log = (stage, data) => {
    logger.info({
      runId,
      stage,
      timestamp: Date.now(),
      elapsed: Date.now() - startTime,
      ...data
    });
  };

  try {
    log('start', { inputSize: input.length });

    const validated = await validate(input);
    log('validated', { valid: true });

    const processed = await process(validated);
    log('processed', {
      tokensUsed: processed.usage.total,
      model: processed.model
    });

    const output = await format(processed);
    log('complete', {
      success: true,
      outputSize: output.length,
      totalTime: Date.now() - startTime
    });

    return output;

  } catch (error) {
    log('error', {
      error: error.message,
      stage: error.stage,
      recoverable: error.recoverable
    });
    throw error;
  }
};
```

## Putting It All Together: A Complete Example

Let's build a document analysis workflow that uses everything we've discussed:

```javascript
class DocumentAnalysisPipeline {
  constructor(options = {}) {
    this.maxChunkSize = options.maxChunkSize || 3000;
    this.concurrency = options.concurrency || 5;
  }

  async run(document) {
    // Stage 1: Validate
    const validated = await this.validate(document);

    // Stage 2: Preprocess
    const chunks = await this.preprocess(validated);

    // Stage 3: Parallel analysis with concurrency control
    const analyses = await this.analyzeChunks(chunks);

    // Stage 4: Synthesize
    const synthesis = await this.synthesize(analyses);

    // Stage 5: Quality check
    const final = await this.qualityCheck(synthesis, document);

    return final;
  }

  async validate(document) {
    if (!document || typeof document !== 'string') {
      throw new ValidationError('Document must be a non-empty string');
    }

    if (document.length > 1000000) {
      throw new ValidationError('Document exceeds maximum size');
    }

    return document;
  }

  async preprocess(document) {
    return chunkDocument(document, {
      maxTokens: this.maxChunkSize,
      overlap: 200
    });
  }

  async analyzeChunks(chunks) {
    const results = [];

    // Process in batches to control concurrency
    for (let i = 0; i < chunks.length; i += this.concurrency) {
      const batch = chunks.slice(i, i + this.concurrency);
      const batchResults = await Promise.all(
        batch.map((chunk, idx) => this.analyzeChunk(chunk, i + idx))
      );
      results.push(...batchResults);
    }

    return results;
  }

  async analyzeChunk(chunk, index) {
    return await withRetry(async () => {
      const result = await llm.call({
        system: `Analyze this document section. Extract:
                 - Key topics and themes
                 - Important facts and figures
                 - Notable quotes or statements
                 - Questions or gaps in information

                 Return structured JSON.`,
        user: `Section ${index + 1}:\n\n${chunk}`
      });

      return parseWithRecovery(result, ANALYSIS_SCHEMA);
    });
  }

  async synthesize(analyses) {
    const combined = analyses.map((a, i) =>
      `Section ${i + 1}:\n${JSON.stringify(a, null, 2)}`
    ).join('\n\n---\n\n');

    return await llm.call({
      system: `Synthesize these section analyses into a comprehensive document summary.
               Structure:
               1. Executive Summary (2-3 sentences)
               2. Key Findings (bullet points)
               3. Important Details
               4. Gaps or Questions
               5. Recommendations`,
      user: combined
    });
  }

  async qualityCheck(synthesis, originalDocument) {
    const check = await llm.call({
      system: `Review this analysis for quality. Check:
               - Accuracy: Does it reflect the original document?
               - Completeness: Are major points covered?
               - Clarity: Is it well-organized and clear?

               Return: {score: 0-1, issues: string[], approved: boolean}`,
      user: `Analysis:\n${synthesis}\n\nOriginal (first 2000 chars):\n${originalDocument.slice(0, 2000)}`
    });

    if (!check.approved) {
      // Log for review but don't fail
      logger.warn({ issues: check.issues, score: check.score });
    }

    return {
      analysis: synthesis,
      qualityScore: check.score,
      qualityIssues: check.issues
    };
  }
}
```

## Common Pitfalls and How to Avoid Them

After building dozens of these systems, here are the mistakes we see most often:

### 1. No Timeout Boundaries

Every LLM call needs a timeout. Set them aggressively.

```javascript
// Bad: No timeout
const result = await llm.call(params);

// Good: Explicit timeout
const result = await Promise.race([
  llm.call(params),
  new Promise((_, reject) =>
    setTimeout(() => reject(new Error('Timeout')), 30000)
  )
]);
```

### 2. Ignoring Token Limits

Track usage and set budgets:

```javascript
const tokenBudget = {
  max: 10000,
  used: 0,

  canSpend(amount) {
    return this.used + amount <= this.max;
  },

  spend(amount) {
    this.used += amount;
    if (this.used > this.max * 0.8) {
      logger.warn('Token budget 80% consumed');
    }
  }
};
```

### 3. No Fallback for Critical Paths

Always have a plan B:

```javascript
const processWithFallback = async (input) => {
  try {
    return await primaryProcess(input);
  } catch (error) {
    if (error.recoverable) {
      return await simplifiedProcess(input);
    }
    // Critical path - queue for manual processing
    await queueForManualReview(input, error);
    return { status: 'queued_for_review' };
  }
};
```

## What's Next

AI workflows are getting more sophisticated. Here's where we see things heading:

- **Better tool use**: Models are getting better at deciding when and how to use external tools
- **Longer context**: Bigger context windows mean fewer chunking headaches
- **Faster inference**: Latency is dropping, enabling more complex real-time workflows
- **Specialized models**: Fine-tuned models for specific tasks outperform general-purpose ones

But the fundamentals don't change. Validate inputs, handle errors gracefully, monitor everything, and always have a fallback. Build on those principles, and your AI workflows will survive contact with the real world.

If you're building AI workflows and hitting walls, we've probably seen your problem before. Reach out, and let's figure it out together.

A hands-on guide to building robust AI pipelines. Learn pipeline architecture, step sequencing, branching logic, error handling, and prompt chaining from engineers who've shipped these systems.

Designing AI Workflows That Actually Work in Production

## The State of E-Commerce Technology

Most e-commerce platforms are stuck in the past. Monolithic systems where every change risks breaking checkout. Templates that look like every other store. Performance that crawls during peak traffic.

We've migrated enough stores off legacy platforms to know the pain. Shopify's limitations hit fast once you grow. Magento's complexity becomes a liability. WooCommerce was never meant for serious scale.

Modern commerce needs a modern approach: **headless architecture, clean data models, and systems built for your business—not the platform's assumptions.**

> Your commerce platform should adapt to your business, not force your business to adapt to it.

This isn't about technology for technology's sake. It's about building commerce systems that actually scale, perform, and evolve with your needs.

## Why Headless Commerce?

In traditional e-commerce, the frontend and backend are fused together. Change the checkout flow? Risk breaking inventory. Add a mobile app? Rebuild everything. Integrate with a marketplace? Good luck.

Headless separates the commerce engine from the presentation layer. The backend handles products, orders, inventory, pricing. The frontend is completely independent—build what you want, how you want.

| Traditional | Headless |
|-------------|----------|
| Frontend locked to platform | Any frontend: web, mobile, kiosk, IoT |
| Changes risk entire system | Change frontend without touching backend |
| Limited customization | Total control over experience |
| Platform dictates UX | You dictate UX |
| One channel | Omnichannel native |

### Real Example: Multi-Channel Retailer

A client sells through web, mobile app, in-store kiosks, and Amazon. With a traditional platform, that's four separate systems with sync nightmares.

With headless:

```
┌─────────────────────────────────────────────┐
│           Commerce Engine (Vendure)         │
│  Products │ Inventory │ Orders │ Customers  │
└─────────────────────────────────────────────┘
              │         │         │
     ┌────────┴───┐ ┌───┴───┐ ┌───┴────┐
     │   Web      │ │ Mobile │ │ Amazon │
     │   (Next.js)│ │ (React │ │  (API) │
     │            │ │ Native)│ │        │
     └────────────┘ └────────┘ └────────┘
```

One source of truth for inventory. One place for orders. Multiple ways to sell.

## Our Platform: Vendure

We build commerce systems on Vendure. Here's why.

**Open Source & Extensible**

Vendure is open source, written in TypeScript, built on Node.js. You own the code. You can modify anything. No license fees eating your margins.

**Built for Customization**

Every business has unique requirements. Vendure's plugin architecture lets us add anything without hacking the core.

```typescript
// Example: Custom plugin for B2B pricing
@VendurePlugin({
  imports: [PluginCommonModule],
  providers: [B2BPricingService],
  configuration: config => {
    config.customFields.Customer.push({
      name: 'priceGroup',
      type: 'string',
      options: [
        { value: 'retail' },
        { value: 'wholesale' },
        { value: 'distributor' }
      ]
    });
    return config;
  }
})
export class B2BPricingPlugin {}
```

**GraphQL Native**

Vendure uses GraphQL by default. Clients request exactly what they need—no over-fetching, no under-fetching.

```graphql
query ProductPage($slug: String!) {
  product(slug: $slug) {
    id
    name
    description
    variants {
      id
      name
      price
      stockLevel
      options {
        name
        value
      }
    }
    assets {
      source
      preview
    }
  }
}
```

### Vendure vs. Alternatives

| Aspect | Vendure | Shopify | Magento | commercetools |
|--------|---------|---------|---------|---------------|
| **Cost** | Open source | Monthly fee + % | License + hosting | Usage-based |
| **Ownership** | Full | Platform-owned | License | Platform |
| **Customization** | Unlimited | Limited | Complex | API only |
| **Tech Stack** | TypeScript/Node | Locked | PHP | Any |
| **Hosting** | Your choice | Shopify only | Your choice | Cloud only |
| **Best For** | Custom builds | Quick start | Enterprise (legacy) | Large enterprise |

## Catalog Management

Your product catalog is the foundation. Get it wrong, and everything downstream suffers.

### Product Data Model

Products aren't simple. They have variants, options, attributes, categories, and relationships. We model this properly from the start.

```typescript
// Product structure example
interface Product {
  id: string;
  name: string;
  slug: string;
  description: string;

  // Variants: actual purchasable items
  variants: ProductVariant[];

  // Options: what differentiates variants
  options: ProductOption[];  // Size, Color, etc.

  // Attributes: searchable/filterable properties
  attributes: ProductAttribute[];  // Brand, Material, etc.

  // Categories: hierarchical organization
  categories: Category[];

  // Assets: images, videos, documents
  assets: Asset[];

  // SEO
  seo: {
    title: string;
    description: string;
    keywords: string[];
  };
}

interface ProductVariant {
  id: string;
  sku: string;
  price: Money;
  stockLevel: number;
  options: SelectedOption[];  // { size: 'L', color: 'Blue' }
}
```

### Faceted Navigation

Customers expect to filter products by multiple attributes simultaneously. This requires proper facet indexing.

| Facet Type | Example | Implementation |
|------------|---------|----------------|
| **Category** | Electronics > Phones | Hierarchical, breadcrumbs |
| **Range** | Price €100-€200 | Min/max aggregation |
| **Multi-select** | Colors: Red, Blue | Array intersection |
| **Boolean** | In stock only | Simple filter |

```typescript
// Search with facets
const results = await search({
  term: 'phone',
  facets: {
    category: ['electronics', 'phones'],
    brand: ['Apple', 'Samsung'],
    priceRange: { min: 500, max: 1000 },
    inStock: true
  },
  sort: { field: 'price', direction: 'ASC' },
  pagination: { page: 1, limit: 24 }
});

// Results include facet counts for UI
results.facets = {
  brand: [
    { value: 'Apple', count: 24 },
    { value: 'Samsung', count: 18 }
  ],
  priceRanges: [
    { range: '500-750', count: 20 },
    { range: '750-1000', count: 22 }
  ]
};
```

## Pricing Architecture

Pricing is where commerce gets complex. Simple retail pricing is the easy case. Real businesses need much more.

### Pricing Models We Support

| Model | Use Case | Complexity |
|-------|----------|------------|
| **Fixed Price** | Standard retail | Low |
| **Customer Group Pricing** | B2B, wholesale | Medium |
| **Volume Pricing** | Buy more, save more | Medium |
| **Dynamic Pricing** | Demand-based, competitor-aware | High |
| **Contract Pricing** | Enterprise agreements | High |
| **Promotional Pricing** | Sales, coupons, bundles | Medium |

```typescript
// Complex pricing calculation
async function calculatePrice(
  product: Product,
  customer: Customer,
  quantity: number,
  context: PriceContext
): Promise<Money> {
  let price = product.basePrice;

  // Customer group discount
  if (customer.priceGroup === 'wholesale') {
    price = applyDiscount(price, customer.discount);
  }

  // Volume pricing
  const volumeTier = getVolumeTier(quantity);
  if (volumeTier) {
    price = applyVolumeDiscount(price, volumeTier);
  }

  // Contract pricing override
  const contractPrice = await getContractPrice(product, customer);
  if (contractPrice && contractPrice < price) {
    price = contractPrice;
  }

  // Active promotions
  const promos = await getActivePromotions(product, context);
  price = applyPromotions(price, promos);

  return price;
}
```

### Multi-Currency

International commerce requires proper currency handling. Not just conversion, but localized pricing.

```typescript
// Currency configuration
const currencyConfig = {
  EUR: { default: true, symbol: '€', position: 'before' },
  USD: { symbol: '$', position: 'before' },
  GBP: { symbol: '£', position: 'before' },
  CHF: { symbol: 'CHF', position: 'after' }
};

// Price per channel/locale
const productPricing = {
  'de-DE': { currency: 'EUR', price: 9900 },  // €99.00
  'en-US': { currency: 'USD', price: 10900 }, // $109.00
  'en-GB': { currency: 'GBP', price: 8500 }   // £85.00
};
```

## Checkout Optimization

Checkout is where money is made or lost. Every friction point costs conversions.

### The Checkout Flow

```
Cart → Customer Info → Shipping → Payment → Confirmation
  │         │             │          │
  └─ Save   └─ Validate   └─ Quote   └─ Process
     state     address       rates      payment
```

### Reducing Friction

| Problem | Solution | Impact |
|---------|----------|--------|
| Too many steps | Single-page checkout | +15% conversion |
| No guest checkout | Guest option prominent | +30% new customers |
| Slow loading | Optimized assets, edge caching | +10% conversion |
| Hidden costs | Show shipping early | -25% cart abandonment |
| Limited payment options | Multiple methods | +10% conversion |

```typescript
// Guest checkout with account creation option
const checkoutFlow = {
  step1_cart: {
    showSummary: true,
    showEstimatedShipping: true,  // No surprises later
    showPromoCode: true
  },

  step2_info: {
    guestCheckout: true,  // Don't force registration
    addressAutocomplete: true,
    rememberDetails: true
  },

  step3_shipping: {
    showAllOptions: true,
    preselect: 'cheapest',  // or 'fastest'
    showDeliveryDates: true
  },

  step4_payment: {
    methods: ['card', 'paypal', 'klarna', 'applepay', 'googlepay'],
    savePaymentMethod: true,  // For logged-in users
    expressCheckout: true
  }
};
```

### Payment Integration

We integrate with multiple payment providers. No single point of failure, no vendor lock-in.

| Provider | Best For | Features |
|----------|----------|----------|
| **Stripe** | General purpose | Cards, wallets, subscriptions |
| **PayPal** | Consumer trust | Pay later, buyer protection |
| **Klarna** | Buy now, pay later | Installments, increased AOV |
| **Adyen** | Enterprise, global | All methods, local acquiring |

## Inventory & Fulfillment

Selling products you don't have ruins customer trust. Inventory management needs to be bulletproof.

### Inventory Tracking

```typescript
interface InventoryLocation {
  id: string;
  name: string;  // 'Main Warehouse', 'Store Berlin'
  type: 'warehouse' | 'store' | 'dropship';
  stock: Map<VariantId, StockLevel>;
}

interface StockLevel {
  available: number;     // Can sell now
  reserved: number;      // In carts, pending orders
  incoming: number;      // On purchase orders
  threshold: number;     // Reorder point
}

// Check availability across locations
async function checkAvailability(
  variant: ProductVariant,
  quantity: number,
  shipTo: Address
): Promise<AvailabilityResult> {
  const locations = await getLocationsByProximity(shipTo);

  for (const location of locations) {
    const stock = await getStock(variant, location);
    if (stock.available >= quantity) {
      return {
        available: true,
        location,
        deliveryEstimate: calculateDelivery(location, shipTo)
      };
    }
  }

  return { available: false, backorderDate: await getBackorderDate(variant) };
}
```

### Order Lifecycle

```
Created → Paid → Processing → Shipped → Delivered
    │       │         │           │          │
    │       │         │           │          └─ Complete
    │       │         │           └─ Tracking update
    │       │         └─ Pick, pack, ship
    │       └─ Payment captured
    └─ Inventory reserved
```

## Scaling for Traffic

Black Friday traffic kills unprepared stores. We build for 10x your normal load.

### Performance Architecture

| Layer | Strategy | Implementation |
|-------|----------|----------------|
| **CDN** | Cache static assets, pages | CloudFront, Fastly |
| **API** | Cache responses, rate limit | Redis, API gateway |
| **Database** | Read replicas, connection pooling | PostgreSQL, PgBouncer |
| **Search** | Dedicated search cluster | Elasticsearch, Algolia |
| **Queue** | Async processing | Redis, SQS |

```typescript
// Caching strategy
const cacheConfig = {
  // Product pages: cache with revalidation
  productPage: {
    ttl: 3600,  // 1 hour
    staleWhileRevalidate: 86400,  // Serve stale for 24h while refreshing
    tags: ['products', 'product:${id}']
  },

  // Cart/checkout: no cache
  checkout: {
    cache: false
  },

  // Category pages: short cache
  categoryPage: {
    ttl: 300,  // 5 minutes
    staleWhileRevalidate: 3600
  }
};
```

### Load Testing

We don't guess about performance. We test before launch.

```bash
# Load test example with k6
k6 run --vus 500 --duration 10m loadtest.js

# Results we target:
# - P95 response time < 200ms
# - Error rate < 0.1%
# - No degradation under 10x normal load
```

## B2B Commerce

B2B commerce has different requirements than B2C. We handle both.

| B2C | B2B |
|-----|-----|
| Fixed prices | Negotiated prices |
| Individual buyers | Buyer organizations |
| Immediate payment | Net terms (30/60/90) |
| Simple checkout | Approval workflows |
| Guest checkout | Account required |

```typescript
// B2B organization structure
interface Organization {
  id: string;
  name: string;
  taxId: string;
  creditLimit: Money;
  paymentTerms: 'net30' | 'net60' | 'net90';

  // Multiple buyers per org
  buyers: Buyer[];

  // Approval workflows
  approvalRules: ApprovalRule[];

  // Custom pricing
  priceList: PriceList;

  // Addresses
  shippingAddresses: Address[];
  billingAddress: Address;
}

// Approval workflow
interface ApprovalRule {
  condition: 'orderTotal > 5000' | 'newVendor';
  approvers: User[];
  requireAll: boolean;
}
```

## Getting Started

Here's how we typically build commerce systems:

**Phase 1: Discovery & Design (2-4 weeks)**
- Understand your business model, products, customers
- Map out integrations (ERP, WMS, PIM)
- Design data model and workflows
- Plan migration strategy if applicable

**Phase 2: Platform Build (8-12 weeks)**
- Vendure setup and customization
- Custom plugins for your requirements
- Integration development
- Admin interface configuration

**Phase 3: Frontend Build (6-10 weeks)**
- Design implementation
- Performance optimization
- Mobile responsiveness
- Accessibility compliance

**Phase 4: Launch & Optimize**
- Load testing and hardening
- Migration execution
- Launch support
- Ongoing optimization

## Conclusion

E-commerce technology should enable your business, not constrain it. With headless architecture and platforms like Vendure, you get full control over your commerce experience while maintaining the flexibility to evolve.

> The best commerce platform is invisible to customers. They just get a fast, smooth experience that makes buying easy.

We've built commerce systems handling millions in transactions. If you're outgrowing your current platform or starting fresh, we'd be happy to discuss what modern commerce could look like for your business.

A comprehensive guide to building modern e-commerce platforms. Learn about headless commerce architecture, Vendure, catalog management, pricing strategies, checkout optimization, and scaling for high-volume sales.

E-Commerce Platforms & Commerce Systems

A comprehensive technical guide to designing and implementing multi-agent systems. Learn agent communication patterns, coordination strategies, task decomposition, specialization, and consensus mechanisms for production environments.

Multi-Agent Architecture: Building Systems That Think Together

## How We Think About Engineering

Good engineering isn't about using the latest framework or following trends. It's about solving problems correctly—building systems that work, scale, and don't fall apart when the original developers leave.

We've inherited enough legacy systems to know what bad engineering looks like. Undocumented code. Brittle architectures. "It works, don't touch it" components that everyone's afraid to modify. Tests that don't run. Deployments that require tribal knowledge.

Our engineering philosophy is simple: **build things right the first time, so you're not rebuilding them forever.**

> The best code is code you don't have to think about. It just works, it's obvious what it does, and it's easy to change.

That doesn't mean over-engineering. It means making the right trade-offs for your situation. Sometimes that's a simple script. Sometimes that's a distributed system. The skill is knowing which one you actually need.

## Full-Stack: What We Actually Do

"Full-stack" is often meaningless buzzword. Here's what it means for us:

| Layer | Technologies | What We Build |
|-------|--------------|---------------|
| **Frontend** | React, Next.js, TypeScript | Web applications, admin interfaces, customer portals |
| **Backend** | Node.js, TypeScript, Python | APIs, services, business logic |
| **Data** | PostgreSQL, Redis, Elasticsearch | Databases, caching, search |
| **Infrastructure** | AWS, GCP, Kubernetes, Terraform | Cloud infrastructure, deployment, scaling |
| **Integration** | REST, GraphQL, WebSockets, queues | System connections, real-time, async processing |

We're not a shop that outsources parts we can't do. When you work with us, one team handles everything. That means fewer handoffs, fewer integration problems, and someone who actually understands the whole system.

## Backend Architecture

The backend is where business logic lives. Get it wrong, and everything else suffers.

### Service Design

We design backends around your domain, not around technical patterns. Services map to business capabilities, not arbitrary technical boundaries.

```typescript
// Good: Service matches business concept
class OrderService {
  async createOrder(cart: Cart, customer: Customer): Promise<Order> {
    // All order-related logic in one place
    const order = await this.validateAndCreate(cart, customer);
    await this.calculatePricing(order);
    await this.reserveInventory(order);
    await this.notifyFulfillment(order);
    return order;
  }
}

// Bad: Technical layers that fragment business logic
class OrderController { /* HTTP only */ }
class OrderRepository { /* DB only */ }
class OrderValidator { /* validation only */ }
// Business logic scattered across files
```

### API Design

APIs are contracts. Once published, they're hard to change. We design them carefully.

**RESTful When It Fits**

For most CRUD operations and straightforward resources, REST is clean and predictable.

```typescript
// Clear, predictable REST endpoints
GET    /api/v1/orders              // List orders
GET    /api/v1/orders/:id          // Get specific order
POST   /api/v1/orders              // Create order
PATCH  /api/v1/orders/:id          // Update order
DELETE /api/v1/orders/:id          // Cancel order

// Nested resources when they make sense
GET    /api/v1/orders/:id/items    // Order line items
POST   /api/v1/orders/:id/refunds  // Create refund
```

**GraphQL When Needed**

For complex, interconnected data with varying client needs, GraphQL shines.

```graphql
# Client requests exactly what they need
query OrderDetails($id: ID!) {
  order(id: $id) {
    id
    status
    total
    items {
      product { name, image }
      quantity
      price
    }
    customer {
      name
      email
    }
    shipments {
      carrier
      tracking
      estimatedDelivery
    }
  }
}
```

### Data Layer

Database design is critical. Poor data modeling causes endless problems.

| Principle | What It Means | Example |
|-----------|---------------|---------|
| **Normalize first** | Reduce redundancy, ensure consistency | Separate customer and order tables |
| **Denormalize for reads** | Add redundancy when query performance demands it | Cache computed totals |
| **Index thoughtfully** | Cover your queries, not everything | Composite indexes for common filters |
| **Plan for scale** | Consider partitioning early | Time-based partitions for logs/events |

```sql
-- Example: Order schema with thought-out indexes
CREATE TABLE orders (
  id UUID PRIMARY KEY DEFAULT gen_random_uuid(),
  customer_id UUID NOT NULL REFERENCES customers(id),
  status VARCHAR(50) NOT NULL DEFAULT 'pending',
  total_cents INTEGER NOT NULL,
  currency VARCHAR(3) NOT NULL DEFAULT 'EUR',
  created_at TIMESTAMPTZ NOT NULL DEFAULT NOW(),
  updated_at TIMESTAMPTZ NOT NULL DEFAULT NOW()
);

-- Indexes for actual query patterns
CREATE INDEX idx_orders_customer ON orders(customer_id);
CREATE INDEX idx_orders_status_created ON orders(status, created_at DESC);
CREATE INDEX idx_orders_created ON orders(created_at DESC);
```

## Frontend Architecture

Frontends get complicated fast. Users expect snappy interfaces, offline support, real-time updates, and flawless mobile experiences. Here's how we deliver.

### Component Architecture

We build frontends from composable, reusable components. Not because it's trendy, but because it actually works.

```typescript
// Component hierarchy for an order page
<OrderPage>
  <OrderHeader order={order} />
  <OrderItems items={order.items} />
  <OrderSummary
    subtotal={order.subtotal}
    shipping={order.shipping}
    tax={order.tax}
    total={order.total}
  />
  <OrderActions
    order={order}
    onCancel={handleCancel}
    onRefund={handleRefund}
  />
</OrderPage>
```

**Smart vs. Dumb Components**

Smart components manage state and logic. Dumb components just render what they're given.

| Type | Has State? | Fetches Data? | Example |
|------|-----------|---------------|---------|
| **Smart (Container)** | Yes | Yes | `<OrderPage>` - manages order state |
| **Dumb (Presentational)** | No | No | `<OrderSummary>` - just displays data |

This separation makes testing easy. Dumb components are trivial to test—just pass props and check output.

### State Management

State management is where many projects go wrong. Either too much (Redux for a todo app) or too little (prop drilling through 10 levels).

Our approach:

```typescript
// Local state for component-specific concerns
const [isOpen, setIsOpen] = useState(false);

// Server state with React Query / SWR
const { data: orders, isLoading } = useQuery({
  queryKey: ['orders'],
  queryFn: fetchOrders
});

// Global state for truly app-wide concerns
// (user session, theme, feature flags)
const { user } = useAuth();
const { theme } = useTheme();
```

### Performance

Slow frontends lose users. We optimize from the start.

| Technique | When to Use | Impact |
|-----------|-------------|--------|
| **Code splitting** | Always | Load only what's needed |
| **Image optimization** | Always | Massive bandwidth savings |
| **Virtualization** | Long lists (100+ items) | Smooth scrolling |
| **Memoization** | Expensive computations | Prevent re-renders |
| **Prefetching** | Predictable navigation | Instant page loads |

```typescript
// Code splitting with Next.js
const HeavyComponent = dynamic(() => import('./HeavyComponent'), {
  loading: () => <Skeleton />,
  ssr: false
});

// Image optimization
<Image
  src="/product.jpg"
  alt="Product"
  width={400}
  height={300}
  placeholder="blur"
  priority={isAboveFold}
/>
```

## Infrastructure & DevOps

Code that can't be deployed reliably isn't done. Infrastructure is part of engineering.

### Infrastructure as Code

Everything is code. Infrastructure, configuration, policies. All version-controlled, all reviewable.

```hcl
# Terraform example: Production-ready EKS cluster
module "eks" {
  source  = "terraform-aws-modules/eks/aws"
  version = "~> 19.0"

  cluster_name    = "production"
  cluster_version = "1.28"

  vpc_id     = module.vpc.vpc_id
  subnet_ids = module.vpc.private_subnets

  eks_managed_node_groups = {
    general = {
      min_size     = 2
      max_size     = 10
      desired_size = 3

      instance_types = ["t3.large"]
      capacity_type  = "ON_DEMAND"
    }
  }
}
```

### CI/CD

Every commit triggers a pipeline. Tests run. Code gets reviewed. Deployments happen automatically.

```yaml
# GitHub Actions pipeline
name: Deploy
on:
  push:
    branches: [main]

jobs:
  test:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4
      - run: npm ci
      - run: npm test
      - run: npm run lint
      - run: npm run type-check

  deploy:
    needs: test
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4
      - name: Deploy to production
        run: |
          # Blue-green deployment
          ./scripts/deploy.sh production
```

### Monitoring & Observability

You can't fix what you can't see. We instrument everything.

| Layer | What We Monitor | Tools |
|-------|-----------------|-------|
| **Application** | Errors, performance, business metrics | Sentry, DataDog, custom |
| **Infrastructure** | CPU, memory, disk, network | CloudWatch, Prometheus |
| **Logs** | Structured logging, searchable | ELK, CloudWatch Logs |
| **Traces** | Request flows across services | Jaeger, X-Ray |
| **Alerts** | Proactive notification | PagerDuty, Slack |

```typescript
// Structured logging example
logger.info('Order created', {
  orderId: order.id,
  customerId: customer.id,
  total: order.total,
  items: order.items.length,
  duration: performance.now() - startTime
});
```

## Engineering Standards

Standards make teams effective. Here's what we enforce.

### Code Quality

| Standard | Why | How |
|----------|-----|-----|
| **TypeScript everywhere** | Catch bugs at compile time | Strict mode, no `any` |
| **Linting** | Consistent style, catch issues | ESLint with strict config |
| **Formatting** | No style debates | Prettier, run on save |
| **Testing** | Confidence in changes | Unit, integration, e2e |

```typescript
// TypeScript strict config
{
  "compilerOptions": {
    "strict": true,
    "noUncheckedIndexedAccess": true,
    "noImplicitReturns": true,
    "noFallthroughCasesInSwitch": true,
    "exactOptionalPropertyTypes": true
  }
}
```

### Code Review

Every change gets reviewed. Not as a formality, but as a genuine quality gate.

**What we look for:**
- Does it solve the right problem?
- Is the approach reasonable?
- Are there edge cases unhandled?
- Is it testable and tested?
- Will future developers understand it?

### Documentation

Code should be self-documenting where possible. But systems need docs.

```
What we document:
├── Architecture decisions (ADRs)
├── API documentation (OpenAPI)
├── Runbooks for operations
├── Onboarding guides
└── Decision rationale (why, not just what)
```

## Security by Default

Security isn't a feature—it's a fundamental requirement.

| Area | Our Approach | Implementation |
|------|--------------|----------------|
| **Authentication** | Industry standards | OAuth 2.0, OIDC, JWT |
| **Authorization** | Principle of least privilege | RBAC, attribute-based |
| **Data** | Encrypt everything | TLS in transit, AES at rest |
| **Secrets** | Never in code | Vault, AWS Secrets Manager |
| **Dependencies** | Stay updated | Automated scanning, updates |

```typescript
// Example: Secure API endpoint
app.post('/api/orders',
  authenticate(),           // Verify identity
  authorize('orders:write'), // Check permission
  validateInput(orderSchema), // Sanitize input
  rateLimit({ max: 100 }),  // Prevent abuse
  async (req, res) => {
    // Business logic here
  }
);
```

## How We Work with Clients

Engineering isn't just about code. It's about solving your problems.

**We start by understanding your business.** Not just the technical requirements, but why they matter. What's the goal? What's the constraint? What happens if we get it wrong?

**We communicate constantly.** Weekly syncs, async updates, transparent access to everything we're building. No surprises.

**We deliver incrementally.** Not a big bang after months of silence, but working software every sprint. You see progress, you can give feedback, you can course-correct.

**We transfer knowledge.** Our goal is for your team to own and maintain everything we build. That means documentation, training, and pair programming when it helps.

## Conclusion

Good software engineering is a craft. It's not about following frameworks blindly or using whatever's newest. It's about making thoughtful decisions, writing code that lasts, and building systems that actually solve problems.

> The best engineering feels boring. Systems that just work. Code that's obvious. Deployments that are non-events.

That's what we deliver. Not exciting demos that fall apart in production, but reliable systems that compound value over time.

If you're building something that matters, we'd be happy to talk.

A comprehensive guide to Oronts' approach to software engineering. Learn about our full-stack development philosophy, backend architecture, frontend systems, infrastructure practices, and engineering standards that deliver robust, maintainable solutions.

Software & Systems Engineering

## Designing Systems That Last

The hardest part of software architecture isn't building systems that work. It's building systems that keep working—through growth, changing requirements, team turnover, and years of maintenance.

We've seen enough architectural disasters to know what doesn't work. The over-engineered microservices that should have been a monolith. The monolith that should have been split years ago. The "scalable" system that can't handle 100 concurrent users.

Good architecture is about making the right trade-offs for your actual situation—not following patterns blindly or preparing for scale you'll never reach.

> The goal isn't the most sophisticated architecture. It's the simplest architecture that solves your problem and can evolve with your business.

## Architectural Principles

Before diving into patterns and technologies, here are the principles that guide our decisions.

### 1. Start Simple, Scale When Needed

Don't build for 10 million users when you have 1,000. That's not planning ahead—it's wasting resources on problems you don't have.

| Stage | Architecture | When to Evolve |
|-------|--------------|----------------|
| **MVP** | Monolith, single DB | Validate the business |
| **Growth** | Monolith, read replicas, caching | Hitting performance limits |
| **Scale** | Service extraction, async processing | Clear bottlenecks identified |
| **Enterprise** | Event-driven, distributed | Org/domain boundaries clear |

### 2. Boring Technology Wins

We use proven, boring technology for critical systems. PostgreSQL over the latest NewSQL database. Node.js over experimental runtimes. Kubernetes over custom orchestration.

```
Boring but reliable:
├── PostgreSQL (not yet another NoSQL)
├── Redis (not experimental caches)
├── Node.js/TypeScript (not experimental languages)
├── React (not framework-of-the-week)
└── Kubernetes (not custom orchestration)
```

Innovation is for the edges where it matters. Core infrastructure should be battle-tested.

### 3. Design for Change

Requirements will change. The architecture should accommodate change without rewrites.

```typescript
// Bad: Hardcoded assumptions
function processOrder(order) {
  charge(order.total);  // What about invoicing? Split payments?
  ship(order.items);    // What about digital goods? Subscriptions?
  email(order.customer); // What about SMS? Push notifications?
}

// Good: Extensible through events
async function processOrder(order) {
  const result = await chargeOrder(order);
  await eventBus.publish('order.paid', { order, result });
  // Listeners handle shipping, notifications, inventory, analytics...
}
```

### 4. Make It Observable

You can't fix what you can't see. Build observability in from the start.

```typescript
// Every service call includes context
async function createOrder(data, context: RequestContext) {
  const span = tracer.startSpan('createOrder', { parent: context.span });

  try {
    const order = await orderService.create(data);

    metrics.increment('orders.created', { channel: data.channel });
    logger.info('Order created', {
      orderId: order.id,
      total: order.total,
      traceId: context.traceId
    });

    return order;
  } catch (error) {
    span.recordException(error);
    throw error;
  } finally {
    span.end();
  }
}
```

## API Design

APIs are the contracts between systems. Once published, they're hard to change. Design them carefully.

### REST Done Right

REST works well for most use cases. The key is consistency.

```typescript
// Consistent patterns across all endpoints
GET    /api/v1/orders           // List (paginated)
GET    /api/v1/orders/:id       // Get one
POST   /api/v1/orders           // Create
PATCH  /api/v1/orders/:id       // Partial update
DELETE /api/v1/orders/:id       // Delete

// Query parameters for filtering/pagination
GET /api/v1/orders?status=pending&page=1&limit=20&sort=-createdAt

// Nested resources when relationship is strong
GET /api/v1/orders/:id/items
POST /api/v1/orders/:id/refunds

// Response format: consistent structure
{
  "data": { ... },
  "meta": {
    "page": 1,
    "limit": 20,
    "total": 156
  }
}
```

### API Versioning

APIs evolve. Plan for it.

| Strategy | When to Use | Trade-offs |
|----------|-------------|------------|
| **URL versioning** (`/v1/`) | Major breaking changes | Clear, but multiple codebases |
| **Header versioning** | More granular control | Less discoverable |
| **No versioning** | Internally only | Simple, but inflexible |

Our default: URL versioning for external APIs, additive changes without versioning when possible.

```typescript
// Additive changes: don't require version bump
// Old response
{ "name": "John" }

// New response (backwards compatible)
{ "name": "John", "email": "john@example.com" }

// Breaking changes: require new version
// v1: { "name": "John Doe" }
// v2: { "firstName": "John", "lastName": "Doe" }
```

### GraphQL When Appropriate

GraphQL excels when clients have varying data needs.

```graphql
# Client specifies exactly what it needs
query OrderSummary($id: ID!) {
  order(id: $id) {
    id
    status
    total
  }
}

query OrderDetails($id: ID!) {
  order(id: $id) {
    id
    status
    total
    items {
      product { name, image }
      quantity
      price
    }
    shipments {
      carrier
      trackingNumber
      status
    }
    history {
      timestamp
      event
      actor
    }
  }
}
```

### API Security

Every API needs proper security. No exceptions.

| Layer | Implementation | Purpose |
|-------|----------------|---------|
| **Transport** | TLS 1.3 | Encrypt in transit |
| **Authentication** | OAuth 2.0 / JWT | Verify identity |
| **Authorization** | RBAC / ABAC | Check permissions |
| **Rate Limiting** | Token bucket | Prevent abuse |
| **Input Validation** | Schema validation | Prevent injection |

```typescript
// Complete API security stack
app.use(helmet());                    // Security headers
app.use(cors(corsOptions));           // CORS policy
app.use(rateLimiter);                 // Rate limiting
app.use(authenticate);                // JWT validation
app.use(validateInput(schema));       // Input validation
app.use(authorize(permissions));      // Permission check
```

## Authentication & Authorization

Auth is critical infrastructure. Get it wrong, and everything is compromised.

### Authentication: Who Are You?

| Method | Use Case | Security Level |
|--------|----------|----------------|
| **Session cookies** | Web apps | High (with proper config) |
| **JWT tokens** | APIs, SPAs | High (short expiry + refresh) |
| **API keys** | Server-to-server | Medium (rotate regularly) |
| **OAuth 2.0** | Third-party access | High (proper flow selection) |

```typescript
// JWT with refresh token flow
interface TokenPair {
  accessToken: string;   // Short-lived: 15 min
  refreshToken: string;  // Longer-lived: 7 days, rotating
}

async function login(credentials): Promise<TokenPair> {
  const user = await validateCredentials(credentials);

  const accessToken = jwt.sign(
    { sub: user.id, roles: user.roles },
    ACCESS_SECRET,
    { expiresIn: '15m' }
  );

  const refreshToken = await createRefreshToken(user.id);

  return { accessToken, refreshToken };
}

async function refresh(refreshToken): Promise<TokenPair> {
  const valid = await validateRefreshToken(refreshToken);
  if (!valid) throw new UnauthorizedError();

  // Rotate refresh token (one-time use)
  await revokeRefreshToken(refreshToken);

  return login(valid.user);
}
```

### Authorization: What Can You Do?

RBAC (Role-Based Access Control) works for most cases. ABAC adds flexibility when needed.

```typescript
// Role-based access control
const roles = {
  admin: ['*'],  // Everything
  manager: ['orders:*', 'products:read', 'customers:read'],
  support: ['orders:read', 'orders:update', 'customers:read'],
  viewer: ['orders:read', 'products:read']
};

function authorize(permission: string) {
  return (req, res, next) => {
    const userPermissions = expandRolePermissions(req.user.roles);

    if (!hasPermission(userPermissions, permission)) {
      return res.status(403).json({ error: 'Forbidden' });
    }

    next();
  };
}

// Usage
app.delete('/orders/:id', authorize('orders:delete'), deleteOrder);
```

## Real-Time Systems

Modern applications need real-time capabilities. Live updates, collaborative features, instant notifications.

### Technology Selection

| Technology | Best For | Trade-offs |
|------------|----------|------------|
| **WebSockets** | Bidirectional, high-frequency | Connection management complexity |
| **Server-Sent Events** | Server→Client updates | Simpler, but one-directional |
| **Long Polling** | Fallback, simple use cases | Higher latency, more requests |
| **WebRTC** | Peer-to-peer, media | Complex, specific use cases |

### WebSocket Architecture

```typescript
// Scalable WebSocket setup with Redis pub/sub
const io = new Server(server, {
  adapter: createAdapter(redisClient)  // Scale across multiple servers
});

// Room-based subscriptions
io.on('connection', (socket) => {
  // Join user's personal room
  socket.join(`user:${socket.user.id}`);

  // Join organization room if B2B
  if (socket.user.orgId) {
    socket.join(`org:${socket.user.orgId}`);
  }
});

// Publish updates from anywhere
async function notifyOrderUpdate(order) {
  // Notify the customer
  io.to(`user:${order.customerId}`).emit('order:updated', order);

  // Notify support team
  io.to('role:support').emit('order:updated', order);
}
```

### Event-Driven Architecture

For complex systems, events decouple components and enable async processing.

```typescript
// Event bus interface
interface EventBus {
  publish(event: string, payload: any): Promise<void>;
  subscribe(event: string, handler: EventHandler): void;
}

// Domain events
type DomainEvent =
  | { type: 'order.created', order: Order }
  | { type: 'order.paid', order: Order, payment: Payment }
  | { type: 'order.shipped', order: Order, shipment: Shipment }
  | { type: 'order.delivered', order: Order };

// Loosely coupled handlers
eventBus.subscribe('order.paid', async (event) => {
  await inventoryService.reserve(event.order.items);
});

eventBus.subscribe('order.paid', async (event) => {
  await notificationService.sendConfirmation(event.order);
});

eventBus.subscribe('order.paid', async (event) => {
  await analyticsService.trackPurchase(event.order);
});
```

## Scaling Strategies

Scaling is about handling growth without rewriting everything.

### Database Scaling

| Strategy | When | Complexity |
|----------|------|------------|
| **Vertical scaling** | First step, up to ~64 cores | Low |
| **Read replicas** | Read-heavy workloads | Low |
| **Connection pooling** | Many app instances | Low |
| **Partitioning** | Time-series, multi-tenant | Medium |
| **Sharding** | Extreme scale | High |

```typescript
// Read replica routing
class Database {
  private primary: Pool;
  private replicas: Pool[];

  async query(sql: string, params: any[], options?: QueryOptions) {
    const pool = options?.readOnly
      ? this.getRandomReplica()
      : this.primary;

    return pool.query(sql, params);
  }

  // Usage
  const order = await db.query(
    'SELECT * FROM orders WHERE id = $1',
    [orderId],
    { readOnly: true }  // Can use replica
  );

  await db.query(
    'UPDATE orders SET status = $1 WHERE id = $2',
    ['shipped', orderId]
    // No readOnly: uses primary
  );
}
```

### Application Scaling

| Pattern | Purpose | Implementation |
|---------|---------|----------------|
| **Horizontal scaling** | Handle more requests | Kubernetes HPA |
| **Caching** | Reduce database load | Redis, CDN |
| **Async processing** | Offload heavy work | Job queues |
| **Circuit breakers** | Handle failures gracefully | Resilience patterns |

```typescript
// Caching strategy
async function getProduct(id: string): Promise<Product> {
  // Check cache first
  const cached = await cache.get(`product:${id}`);
  if (cached) return cached;

  // Cache miss: fetch from DB
  const product = await db.query('SELECT * FROM products WHERE id = $1', [id]);

  // Cache for future requests
  await cache.set(`product:${id}`, product, { ttl: 3600 });

  return product;
}

// Cache invalidation on update
async function updateProduct(id: string, data: Partial<Product>) {
  await db.query('UPDATE products SET ...', [data, id]);
  await cache.delete(`product:${id}`);
  await cache.delete('product-list:*');  // Invalidate list caches
}
```

## Infrastructure Patterns

How you run your software matters as much as how you write it.

### Container Orchestration

Kubernetes has become the standard. Here's a production-ready setup:

```yaml
# Deployment with best practices
apiVersion: apps/v1
kind: Deployment
metadata:
  name: api
spec:
  replicas: 3
  strategy:
    type: RollingUpdate
    rollingUpdate:
      maxUnavailable: 1
      maxSurge: 1
  template:
    spec:
      containers:
      - name: api
        image: api:v1.2.3
        resources:
          requests:
            memory: "256Mi"
            cpu: "250m"
          limits:
            memory: "512Mi"
            cpu: "500m"
        livenessProbe:
          httpGet:
            path: /health
            port: 3000
          initialDelaySeconds: 10
          periodSeconds: 10
        readinessProbe:
          httpGet:
            path: /ready
            port: 3000
          initialDelaySeconds: 5
          periodSeconds: 5
```

### Multi-Region Deployment

For global availability and performance:

```
                    ┌─────────────┐
                    │   Global    │
                    │  DNS/CDN    │
                    └──────┬──────┘
                           │
        ┌──────────────────┼──────────────────┐
        │                  │                  │
   ┌────▼────┐        ┌────▼────┐        ┌────▼────┐
   │ EU-WEST │        │ US-EAST │        │ AP-SOUTH│
   │ Region  │        │ Region  │        │ Region  │
   └────┬────┘        └────┬────┘        └────┬────┘
        │                  │                  │
   ┌────▼────┐        ┌────▼────┐        ┌────▼────┐
   │   DB    │◄──────►│   DB    │◄──────►│   DB    │
   │(Primary)│        │(Replica)│        │(Replica)│
   └─────────┘        └─────────┘        └─────────┘
```

## Monolith vs. Microservices

The eternal debate. Here's our take:

| Start with Monolith When | Consider Microservices When |
|--------------------------|----------------------------|
| New product, uncertain requirements | Clear domain boundaries exist |
| Small team (< 10 engineers) | Multiple teams need independence |
| Need to move fast | Different scaling requirements per service |
| Don't know your domains yet | Different technology needs per service |

### The Modular Monolith

Best of both worlds: monolith deployment simplicity with service-like boundaries.

```
src/
├── modules/
│   ├── orders/           # Order domain
│   │   ├── api/          # HTTP handlers
│   │   ├── services/     # Business logic
│   │   ├── repository/   # Data access
│   │   └── events/       # Domain events
│   │
│   ├── inventory/        # Inventory domain
│   │   ├── api/
│   │   ├── services/
│   │   └── ...
│   │
│   └── customers/        # Customer domain
│       └── ...
│
├── shared/               # Cross-cutting concerns
│   ├── database/
│   ├── auth/
│   └── events/
│
└── main.ts              # Single deployment
```

Modules communicate through well-defined interfaces. When you need to extract a service, the boundaries are already there.

## Disaster Recovery

Systems fail. Plan for it.

| Level | RTO | RPO | Implementation |
|-------|-----|-----|----------------|
| **Basic** | Hours | Hours | Daily backups, manual restore |
| **Standard** | 30 min | 15 min | Automated backups, standby DB |
| **High Availability** | Minutes | Minutes | Multi-AZ, automated failover |
| **Mission Critical** | Seconds | Near-zero | Multi-region, active-active |

```yaml
# Backup configuration example
backups:
  database:
    frequency: "every 6 hours"
    retention: "30 days"
    type: "point-in-time recovery"
    destination: "s3://backups/postgres"

  application:
    frequency: "every deployment"
    retention: "90 days"
    type: "container images"
    destination: "ecr://app-images"

  documents:
    frequency: "real-time"
    retention: "indefinite"
    type: "object versioning"
    destination: "s3://documents"
```

## Conclusion

Good architecture isn't about following trends or implementing every pattern you've read about. It's about understanding your actual needs and making deliberate trade-offs.

> The best architectures are the simplest ones that solve the problem. Complexity is a cost, not a feature.

We've designed systems handling millions of requests, processing real-time data, and serving global users. The common thread isn't sophisticated technology—it's thoughtful design that matches the actual requirements.

If you're facing architectural decisions or struggling with systems that don't scale, we'd be happy to discuss your specific situation.

A comprehensive guide to designing systems that last. Learn about architectural patterns, API design, authentication systems, real-time infrastructure, and building for scale without over-engineering.

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