AI Deployment

Introduction

Deploying agents means shipping them as services with autoscaling, queues, secrets, monitoring and rollbacks. Common stacks: Vercel + Inngest, AWS Lambda + SQS, Cloudflare Workers, GCP Cloud Run + Pub/Sub.

Beginner analogy: running a restaurant — kitchen (workers), tickets (queue), waiters (API), manager (orchestrator).

Understanding the topic

Core concepts:

• Stateless API + worker pool.
• Queue for long agent runs.
• Secrets in a vault, never in env files.
• Health checks + structured logs.
• Blue/green or canary rollouts.

Syntax reference

Visual workflow / architecture:

┌──────────┐  ┌──────────┐  ┌──────────┐
│  Client  │─►│ API GW   │─►│  Agent   │
└──────────┘  └──────────┘  └─────┬────┘
                                  │
            ┌─────────────────────┼─────────────────────┐
            ▼                     ▼                     ▼
       ┌────────┐           ┌──────────┐         ┌──────────┐
       │ LLM API│           │ Vector DB│         │  Tools   │
       └────────┘           └──────────┘         └──────────┘
            │                     │                     │
            └──────► Logs · Traces · Cost · Eval ◄──────┘

Real-world use

OpenAI Operator runs on AWS; Cursor on Vercel + Anthropic; Devin uses Kubernetes + custom infra.

Best practices

• Use queues for any > 5 s task.
• Canary deploy new prompts before global.

Common mistakes

• In-request agent runs > 30 s — request timeouts.

Hands-on exercise

Interview preparation — practice these questions:

• Q1. Why queue long agent runs?
• Q2. Three deployment stacks for agents.
• Q3. Scenario: your agent takes 2 min/run. How do you architect?