Production AI Agent — Scalable Deployment & Monitoring

I will build production infrastructure for your AI agent — covering a job queue for agent task submission, worker pool for parallel agent execution, retry logic with exponential backoff for transient failures, dead letter queue for permanently failed tasks, rate limiting to manage LLM API costs, and a monitoring stack tracking queue depth, worker utilisation, and per-task success rates. An agent that works reliably in a single-user demo frequently fails under production load — without a job queue, concurrent requests overwhelm available LLM API capacity; without retry logic, transient API errors cause permanent task failures; without monitoring, queue backlog accumulates invisibly until users start complaining.

The infrastructure covers a message queue (Redis, RabbitMQ, or SQS), worker pool with configurable concurrency, exponential backoff retry, dead letter queue, LLM rate limit management, a monitoring dashboard, and Docker deployment configuration.