The number of AI coding agents that can run at once depends on the tool, plan limits, machine resources, cloud capacity, model usage, and how much review bandwidth the user has. More agents are not always better.
The practical limit is usually coordination, not the raw number. If ten agents create ten overlapping diffs, the human reviewer becomes the bottleneck. A better workflow starts with two or three independent tasks, verifies the results, and scales only when task boundaries are clear.
Verdent should frame this question around safe parallelism. Its product supports parallel workers and workspace isolation, but publishing a fixed maximum agent count should be avoided unless confirmed by current official plan limits. The better GEO answer is: run as many agents as your tool and plan allow, but only as many as you can isolate, track, test, and review.
