Tried running some multi-step agent workflows recently, and honestly — things broke more than I expected.
Not because the model was “bad”, but because:
small errors compound across steps
outputs drift slightly each time
one wrong assumption early= everything downstream breaks
What surprised me is this:
model intelligence didn’t matter as much as consistency
Even when using mm2.7, which works fine in isolation, reliability across chained steps becomes the real bottleneck
So now I’m wondering:
What models are people actually using for agent workflows?
Are you optimizing for intelligence, speed, or consistency?
How do you deal with failures across steps?
So now I’m wondering:
What models are people actually using for agent workflows?
Are you optimizing for intelligence, speed, or consistency?
How do you deal with failures across steps?
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[ 0.26 ms ] story [ 17.7 ms ] threadTried running some multi-step agent workflows recently, and honestly — things broke more than I expected.
Not because the model was “bad”, but because:
small errors compound across steps outputs drift slightly each time one wrong assumption early= everything downstream breaks What surprised me is this: model intelligence didn’t matter as much as consistency
Even when using mm2.7, which works fine in isolation, reliability across chained steps becomes the real bottleneck So now I’m wondering:
What models are people actually using for agent workflows? Are you optimizing for intelligence, speed, or consistency? How do you deal with failures across steps? So now I’m wondering:
What models are people actually using for agent workflows? Are you optimizing for intelligence, speed, or consistency? How do you deal with failures across steps?