At what point does running more agents backfire?
by•
I keep seeing tools talk about how many agents they can run in parallel or how much of the backlog they can take on.
But every additional agent also creates more work to review, more decisions to make and more places where something can quietly go wrong.
At some point, the person supervising them is no longer properly checking the work. They are just trying to keep up with it.
So I’m starting to think agent count is the wrong measure. What matters is how much verified work gets completed without creating another pile of work for the human overseeing it.
Has anyone found where that tipping point is?
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I’ve started thinking about agents more like junior teammates. Adding more only helps if I still have enough time to review what they are doing.
@sharon_owens There’s definitely a point where parallel execution starts feeling like managing a very busy inbox. My goal would be fewer things needing my attention, not simply more things happening.
For me, reliability would matter more than the number of agents running.
Promomix
More agents make sense when they reduce waiting. If they increase context switching i would rather run fewer.
I'm curious how others are deciding when to stop adding agents.
For me two focused agents beat five noisy ones. context overlap creates a surprisingly big review tax.
i wonder if smaller teams hit this limit much earlier.
Buffup.AI
I would measure this by accepted work vs. review work generated. that tells you whether the extra agents are actually helping.
Bababot
The dangerous part is that the extra workload doesnt always look like work its tiny decisions, reviews, conflicts, and corrections pilling up.
it backfired on us when an agent put a confident number into a report that nobody measured. other agents took it as ground truth and we spent a day tracing what was just a placholder ) more agents fail the moment they start verifying each other's hallucinations instead of real data