most multi-agent failures aren't what they look like. the agent did something wrong β but the model was fine. the bug lived in a layer the model never saw.
a broadcast that skips because a db transaction ended and took the org context with it. an upload that 403s because a signed header wasn't echoed to the client. a write that lands on zero rows and looks like a clean commit.
none of these are intelligence problems. a smarter model navigating a context-less read just misses the same rows with more confidence.
the assumption that keeps breaking is always the same: one session, one transaction, one principal. every layer of existing infrastructure was built around it. concurrent agents don't bend that assumption β they snap it, silently, with a green status.
the teams that actually scale multi-agent systems will have found every place that assumption lived and made it loud, explicit, or structurally impossible to violate. that's the work.
@gabepereyra@appliedcompute What is the cost difference between training and running these models versus using top-of-the-line base models?
I am asking since there is increasing talks about cost efficiency, usage of models / tokens