Towards Risk-free AI Agent Deployment

This position paper argues that enforcing LLM agent safety within a single abstraction layer is not merely suboptimal but categorically insufficient for deployed LLM agents -- a structural consequence of how agent execution works, not a contingent limitation of current systems. The three dimensions that jointly constitute safe operation -- semantic intent and policy compliance, environmental validity, and dynamical feasibility -- each depend on a strictly distinct set of information that becomes available at different stages of execution. No single guardrail can certify all three. We argue that the community must respond with a contract-based architecture in which each safety dimension is enforced by an independently certified layer whose probabilistic guarantee satisfies the next layer's assumption. We sketch such an architecture and derive the compositional system-level safety bounds it admits via the chain rule of probability. Three open problems stand between this and a deployable standard: bound estimation from non-i.i.d.\ traces, graceful degradation of contracts under deployment drift, and extension to multi-agent settings -- the most important unfinished business in LLM agent runtime assurance.

Large Language Models (LLMs) are increasingly deployed as agents that operate in real-world environments, introducing safety risks beyond linguistic harm through tool use, external transactions, and multi-step decision making. Existing agent safety evaluations construct risk-oriented tasks that lack a hierarchically organized taxonomy of attack surfaces, rely on predefined harm categories rather than deployment-grounded safety rubrics, and evaluate agent behavior in isolation rather than during realistic, long-horizon task execution. To address these limitations, we propose Risky-Bench, a deployment-grounded framework for systematic agent safety evaluation in life-assist settings. Risky-Bench derives context-aware safety rubrics from domain-agnostic safety principles and systematically probes safety risks across hierarchically organized attack surfaces through realistic task execution under varying threat assumptions. Applied to life-assist scenarios covering food delivery, in-store assistance, and online travel booking, Risky-Bench evaluates seven state-of-the-art agents across 750 tasks, revealing attack success rates ranging from 25% to 60% and exposing systematic failure patterns including threat-model-specific vulnerabilities, attack-surface-dependent risk concentration, and model-dependent effects of explicit reasoning. Our code and data are available at https://github.com/SophieZheng998/Risky-Bench.

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 deployments: Safety and Robustness, and Privacy and System Security. For each dimension, we clarify key concepts, identify where risks emerge along the agent workflow, and summarize stage-targeted mitigation strategies. Other trustworthiness aspects (value alignment, transparency, fairness, and accountability) are discussed as relevant context rather than parallel chapters. To support consistent comparison and deployment decisions, we consolidate evaluation into a unified metrics-and-benchmarks hub, emphasizing both outcome and process signals (e.g., constraint violations, trace completeness, and adversarial success rates) and offering scenario-to-metric guidance for release gating. We conclude by outlining open challenges such as self-evolving agents, runtime monitoring and verification, privacy-preserving personalization, and the trust-utility trade-off, and present a case study of real-world security failures in open-source agentic systems. Our goal is to serve as a practical reference for researchers and practitioners building trustworthy agentic systems in high-stakes environments.