A correct answer that arrives too late is still a product failure.
Why it matters
Agent latency compounds. A few model calls, retrieval passes, tool calls, and retries can turn a simple request into a long wait. Provider limits add more pressure. Production systems need concurrency control, progressive feedback, caching, fallback models, and hard deadlines.
Build this
- Timeouts and deadlines for model calls, tools, workflow steps, and whole tasks.
- Queues, concurrency caps, exponential backoff, and provider-specific rate limit handling.
- Streaming, progress events, partial results, and background completion for slow paths.
- Caches for stable retrieval, prompt prefixes, tool results, and deterministic computations.
Watch for
- Retry storms after a provider starts throttling.
- Parallel agents that multiply rate limit pressure without improving user-perceived speed.
- Long context windows used as the default path for small tasks.
- A UI that looks frozen while a long-running task is still healthy.
Proof it works
- Load tests include provider throttling, slow tools, and retry behaviour.
- The product has a user-visible state for queued, running, waiting for review, failed, and complete.
- Metrics separate model latency, tool latency, queue time, and orchestration overhead.
Implementation checklist
01
Set a latency budget per task class before choosing models and tools.
02
Prefer fast routing for simple tasks and escalation for hard tasks.
03
Use backpressure and queue depth alerts instead of letting requests pile up invisibly.
04
Cache only when correctness, freshness, and invalidation are explicit.