Scalability, availability, reliability, and latency

🔴 MUST KNOW · Begin with workload, risk, and successful outcomes; choose tools after that.

Overview

• Scalability handles growing load.
• Availability means the system is accessible when needed; reliability means tasks produce correct, durable outcomes despite failures.
• Latency measures waiting, including queue time and user-visible completion.

Why this matters

A demo with one user does not expose provider quotas, worker contention, slow tools, or review bottlenecks.

An everyday example

A restaurant must serve many customers, stay open, deliver correct meals, and avoid excessive waits.

A technical example

Stateless APIs scale horizontally, while bounded workers, provider admission controls, and thread serialization protect constrained dependencies.

How it works

1. Estimate arrivals and call amplification
2. Identify bottlenecks
3. Isolate traffic
4. Scale appropriate services
5. Cap queues
6. Degrade safely
7. Monitor objectives.

Architecture and flow

Ingress → quota → stateless API → bounded model/tool work or queue → durable result.

The insurance assistant example

Peak claim traffic after a storm must not overload provider limits or produce duplicate submissions.

When to use it

Every production design should specify quality, latency, availability, and capacity targets.

When to avoid it

Do not add distributed components before measuring the bottleneck or claim redundancy while all paths share one failed provider.

Interview answer

“I scale by workload and dependency capacity, bound agent amplification, and define safe degraded behavior while preserving durable business outcomes.”

Benefits, trade-offs, and production considerations

• As a rough sizing tool, Little's law says in-flight work is approximately arrival rate times average duration.
• At 20 requests/second and 3 seconds average service time, roughly 60 requests are in flight before bursts/retries.
• This is not a complete worker or quota model.