Phase 8: Production Agent Infrastructure · 65 min · Python · FastAPI · Redis
Queue-Based Agent Dispatch
1000 users, 1 agent server. The queue is the equalizer.
Hiring signal: Queue-based architectures are universal for any async workload beyond the simplest apps. Any company running AI tasks for many users needs this — every job posting for senior backend or AI engineer includes it. Demonstrated queue implementation + scaling knowledge is table-stakes for senior roles.
What you will learn
- Implement a task queue using Redis (via RQ) for agent job dispatch
- Design an async job status API: submit, poll, retrieve result
- Scale horizontally: add workers to increase throughput
The Problem
Direct HTTP requests to an agent have a fundamental scaling problem: the HTTP connection stays open while the agent works. For tasks that take 30 seconds, 2 minutes, or longer, this breaks down completely. Load balancers time out (typically 60 seconds). Mobile connections drop. The server can only handle as many concurrent users as it has threads.
Queue-based architecture solves this. Users submit tasks and get a job_id immediately. Workers pull tasks from the queue and process them independently. Users poll for status or receive a webhook when their job completes.
Queue-Based Architecture
The system has four components:
Queue (Redis): Stores pending tasks as serialized payloads. Tasks wait here until a worker picks them up. Redis supports atomic operations, so two workers can never accidentally pick the same task.
Producer (API server): Accepts task submissions from users, validates them, enqueues them, and returns a job_id immediately with HTTP 202 Accepted. The producer doesn't run the agent — it just queues work.
Workers (N agent processes): Each worker process independently loops: pull task from queue → run agent → store result → pull next task. Workers are stateless — you can add or remove them without coordination.
Result Store (Redis or database): Maps job_id → {status, result, timestamps}. The status API reads from here.
A user submits a 3-minute agent task. What's the correct API design?
HTTP connections should not be held open for minutes — load balancers time out (typically 60s), mobile connections drop, and the UX is just a spinning UI. The async job pattern: accept immediately (202), process in background, notify or let the user poll. This is the standard pattern for any operation that takes more than 5 seconds.
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You've read the first 2 sections. The rest of this lesson covers Job Lifecycle and Status Tracking, Horizontal Scaling, Build It, What to Practice — plus a hands-on lab, quiz, and project artifact.
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