AI agents that call real tools — deleting records, sending payments, editing files — don't usually fail by misunderstanding instructions in bulk. They fail by issuing one bad call in an otherwise-correct session. An eval score of 98% is no comfort if you're the run in the 2%, and no eval can tell you, in the moment, whether the call on the stack right now is that one. That's the specific problem I built ACEL to solve: a runtime verification layer that sits between an agent and its to...