BLACK LABELAcademy

One Agent or Many? When to Fan Out (and When It Burns You)

intermediate6 min read · updated 2026-06-20

Modern AI can run many agents in parallel. It's tempting to think more agents = faster. Often

the opposite is true. Knowing when to use one focused agent versus a fleet is one of the highest-

leverage skills in working with AI — and getting it wrong is expensive.

The rule of thumb

Same file / same state → ONE writer. When work edits the same thing, parallel agents

collide and leave half-finished, contradictory changes. Do it with a single agent, in sequence.

Independent work → fan out. When tasks truly don't touch each other (research 100 separate

topics, review 20 unrelated files), parallel agents are a superpower — many at once, then merge.

A 87-agent fleet once drained our shared usage limit and killed a scheduled meeting; multiple

agents editing the same code left mid-edit breakage. We replaced it with a single relay that

runs agents one at a time in a circle — a dead agent can't dead-end the whole thing.

Even correct fan-out has limits: building this app's content with a big parallel fleet kept

tripping a server-side rate limit, so a chunk of work had to be done by a single writer inline

instead. More parallelism than the system (or your budget) can absorb just fails faster.

Default to one focused agent. Only fan out when you can point to genuinely independent

pieces with no shared state.

For big independent batches, parallelize (research, drafting many separate items, broad

reviews) — that's where many-agents shines.

Watch the shared limits. Parallel agents share your usage/rate budget; a giant burst can

throttle everything. Scale the fan-out to what the system can actually take.

Make loops un-stuck-able. If you run agents in sequence, design it so one failing agent

can't halt the rest (a relay/queue, not a fragile chain).

When in doubt, sequence it. Slower-but-correct beats fast-but-broken almost every time

with AI.