Free Lesson

Your AI Agent Works for 10 Users. What Happens at 10,000?

45 min

Aug 27, 2026 11:00 AM

What you'll learn

Identify Where Agent Systems Break at Scale

Understand model limits, tool bottlenecks, database contention, queue buildup, state-mgmt issues and cascading failures

Design Scalable Agent Execution Architectures

Use queues, asynchronous workers, parallel execution, state stores & distributed orchestration to scale agent workflows

Control Latency, Throughput and Cost

Apply batching, caching, model routing, concurrency controls & workload prioritization to keep economics sustainable

Build Backpressure and Failure Isolation

Prevent overload from cascading through the system using rate limits, circuit breakers and workload isolation

Why this topic matters

An agent that works well in a demo may fail completely under production load. As usage grows, teams encounter model rate limits, API throttling, queue buildup, database contention, workflow-state problems, long-tail latency and rapidly increasing inference cost. Scaling agents requires more than adding servers. It requires a production architecture designed for concurrency, backpressure, distributed execution, state, reliability and economics.