Why run tools in parallel
When several tool calls do not depend on each other, running them one after another wastes time. Parallel execution fires all the independent calls at once and waits for them together, cutting total latency.
When it is safe
- The calls are independent: none needs another call result as input.
- The tools are read only or otherwise free of conflicting side effects.
- The agent can fan in the results before deciding the next step.
If call B needs the output of call A, they must stay sequential.
How an agent decides
A capable model can emit several tool calls in one turn. The runtime dispatches them concurrently, collects every result, and feeds the whole batch back as observations.
Pitfalls
- Hidden dependencies: parallelizing calls that actually depend on each other yields wrong results.
- Side effects: two writes at once can race or conflict.
- Error handling: one call may fail while others succeed, so the agent must reconcile a partial batch.
Used carefully, parallel tool calls turn a slow chain of lookups into a single fast round trip.
Key idea
Parallel tool execution dispatches independent side effect free tool calls at once and fans the results back together, cutting latency, but only when there are no hidden dependencies or conflicting writes.