Optimize the complete task path while preserving quality, safety, and user experience.
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GenAI latency and cost accumulate across model calls, retrieval, tools, retries, and agent loops. Scaling is therefore a workload-control problem as much as an infrastructure problem.
Primary Levers
Stream partial output when early feedback is useful.
Queue long-running tasks and make them resumable.
Limit concurrency and apply backpressure before providers or tools saturate.
Cache stable prompt prefixes and repeated retrieval results where valid.
Batch asynchronous workloads when latency permits.
Reduce irrelevant context and cap agent iterations.
Route bounded tasks to models that meet the measured quality threshold.
Measure the Whole Task
Track time to first useful output, end-to-end completion time, success rate, retry rate, tool latency, token use, and cost per successful task. A lower token count is not an optimization if it reduces groundedness or increases escalation.
Optimization order
Remove unnecessary work first, choose the right architecture second, route models third, and tune infrastructure after the dominant bottleneck is known.
Key Takeaways
Scale the end-to-end task, not only the model endpoint.
Backpressure, bounded loops, caching, batching, and routing address different bottlenecks.
Optimize cost per successful task under quality and safety constraints.
An agent repeatedly calls the same tool until a provider limit is hit. What is the first architectural fix?