Batching Agent Calls The Latency Win

Sequential model calls are the most common agent latency tax — and the easiest to fix. Batching turns N round-trips into one, and the latency win is often the largest single optimization in an agent workflow.

Agents that call the model in a loop — one item at a time — pay the round-trip latency N times. Batching collapses the loop into one call, and the latency drops by the number of items, not by a percentage.

The delta is real: N sequential calls take roughly N round-trips of latency; one batched call takes one. For workflows that process items in a loop, batching is the highest-ROI latency win — and the cheapest to implement.

• N sequential calls → 1 batched call.
• Latency drops by the batch factor.
• Cost stays the same (you still process N items).

Batching wins when the items are independent — when each call doesn't depend on the previous one's output. Classification, extraction, and scoring are usually independent and batch well; chained reasoning is dependent and doesn't.

• Independent items: batch.
• Dependent items: keep sequential.
• Mixed: batch the independent steps, sequence the dependent ones.

• Batching changes failure semantics — one bad item can fail the batch, or be handled per-item.
• Token limits cap the batch size; very large batches may need chunking.
• The model may behave differently on batched input — eval before and after.

• Batching dependent calls (it breaks the logic).
• Batching without an eval (batching can change output quality).
• Batching beyond the token limit without chunking.

Batching is the highest-ROI latency win for agents that call the model in a loop. Batch the independent calls, sequence the dependent ones, and eval before and after — and the latency drops by the batch factor.