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Agents, Automata, and the Memory of Thought

A booking agent is not dangerous because it can “reason.” It is dangerous because it can remember the wrong thing, forget the right thing, loop politely forever, or book the flight before the human has actually confirmed. The philosophy department may enjoy debating whether this counts as intention. The operations team has a simpler question: can we know, before deployment, what behaviours this system can produce? ...

November 1, 2025 · 15 min · Zelina
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Forkcast: How Pro2Guard Predicts and Prevents LLM Agent Failures

TL;DR for operators ProbGuard1 is a runtime safety monitor that tries to answer a more useful question than “Has the agent broken a rule?” It asks: “Given where the agent is now, how likely is it to end up breaking a rule soon?” That shift matters. Many agent failures are not single bad actions. They are bad trajectories: the robot chooses the wrong object, the car carries too much speed into a risky scene, the workflow skips a confirmation step three moves before data is exposed. A conventional rule-based guardrail often detects the problem when the violation is already visible. ProbGuard tries to detect the probability mass moving toward the violation earlier. ...

August 4, 2025 · 17 min · Zelina