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Architecture patterns for scaling agent fleets from prototype to production at volume.
Read through the lesson, mark it complete when the concept is clear, then move to the next lesson in the sequence or jump back to the module map.
Agent systems scale in discontinuous jumps. A system that works for 10 tasks/day behaves differently at 1,000 tasks/day. Plan for these inflection points before you hit them:
At scale, every task goes through a queue:
Client → Task Queue → Worker Pool → Result Store → Client
Benefits: decoupled load, retry logic, prioritization, visibility into backlog, horizontal scaling.
Workers pull tasks from the queue, execute, and write results. The client polls or subscribes for results.
Use a worker pool with autoscaling. Key parameters:
Match worker count to queue depth, not to raw task volume.
Don't use in-memory state for anything that matters. Persist to a database with atomic operations. Common failure mode: two workers processing the same task simultaneously — use database-level locking or idempotency keys to prevent this.
At scale, cost optimization becomes a first-class concern:
10% of tasks often account for 50% of cost. Find them and optimize them specifically.