AI agents that call real tools — deleting records, sending payments, editing files — don't usually...
ACEL blocks unsafe AI agent tool calls before execution using LTL3-monitored safety contracts. For enterprises, this shifts governance from statistical evals (98% average) to hard call-level enforcement—violations blocked before side effects, decoupling safety from model behavior.
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
I built 5 mechanical gates for my AI coding agent. Then a philosopher told me I was solving the wrong...
Most teams can get an AI agent to call tools. The hard part starts when the tool can move money,...
Build an AI agent runtime policy layer that checks tool calls, scopes, spend, secrets, and approvals before agents touch…
Tool-using agents often act on what they think is true rather than what they've checked. A new design forces the agent to keep a…
AI-first devtools need executable CLI contracts: typed schemas, deterministic JSON, local evidence, and handles agents can verify.
There's a pattern I kept seeing. A team gives an agent real capability, like moving money, shipping a...
