Parallel Execution (Agents) — AI Glossary

Reference details

Related terms

• DAG (Directed Acyclic Graph)
• Task Decomposition
• Parallel Tool Calls

How it works

1. The dependency graph is established first: two steps can run together only if neither consumes the other's output.
2. Independent branches are dispatched concurrently as separate calls rather than as sequential turns in one conversation.
3. Each branch gets its own context, which is why parallelism costs tokens — shared preamble is paid for once per branch, not once in total.
4. Results are joined at a barrier, and the join must handle partial failure: one branch erroring cannot be allowed to discard the others.
5. Total latency becomes the slowest branch plus the join, so the gain is bounded by the longest branch and not by the average.

Example: What parallelism buys, and what it costs

Five independent lookups take 3, 4, 4, 6 and 8 seconds. Sequentially that is 25 seconds; in parallel it is the slowest, 8, plus the join — a little over threefold. Tokens move the other way: each branch repeats a 400-token preamble, so 5 × 400 = 2,000 tokens of context against 400 if one call handled all five. The trade is explicit — roughly 3× the speed for roughly 5× the preamble — and if a sixth lookup depends on the first's result it cannot join the fan-out at all.

Commonly confused with

Confused with batching, which is the same word applied at a different layer. Batching is a serving optimisation: the inference engine groups unrelated requests to use the accelerator better, and callers do not know it happened. Parallel execution is an orchestration decision: the application issues several calls at once because their work is independent. Batching improves throughput and does not reduce any single request's latency; parallel execution reduces a task's wall-clock latency and increases total tokens. They compose, and confusing them leads to expecting a latency win from the one that cannot give it.

When to use it

Reach for it when:

• Several sub-tasks are genuinely independent and latency matters more than token cost.
• A research or review step fans out over sources that do not inform each other.
• The slowest branch is much shorter than the sum, so the ceiling is worth having.

Reach for something else when:

• Steps are chained, where fan-out produces work based on context that does not exist yet.
• Token cost dominates, since duplicated preamble scales with the branch count.
• One branch dominates the runtime, leaving little to recover.

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