Agentic AI systems, which build on Large Language Models (LLMs) and interact with tools and memory, have rapidly advanced in capability and scope. Yet, since LLMs have been shown to struggle in multilingual settings, typically resulting in lower performance and reduced safety, agentic systems risk inheriting these limitations. This raises concerns about the accessibility of such systems, as users interacting in languages other than English may encounter unreliable or security-critical agent behavior. Despite growing interest in evaluating agentic AI and recent initial efforts toward multilingual interaction, existing benchmarks do not yet provide a comprehensive, multi-domain, security-aware evaluation of multilingual agentic systems. To address this gap, we propose MAPS, a multilingual benchmark suite designed to evaluate agentic AI systems across diverse languages and tasks. MAPS builds on four widely used agentic benchmarks — GAIA (real-world tasks), SWE-Bench (code generation), MATH (mathematical reasoning), and the Agent Security Benchmark (security). We translate each dataset into eleven diverse languages, resulting in 805 unique tasks and 9,660 total language-specific instances - enabling a systematic analysis of the Multilingual Effect on AI agents’ performance and robustness. Empirically, we observe a degradation in both performance and security when transitioning from English to other languages, with severity varying by task and correlating with the amount of translated input. This work establishes the first standardized evaluation framework for multilingual agentic AI, encouraging future research towards equitable, reliable, and accessible agentic AI. https://huggingface.co/datasets/Fujitsu-FRE/MAPS

Large language models (LLMs) have demonstrated impressive performance across a wide range of tasks, including open-ended dialogue, driving advancements in virtual assistants and other interactive systems. However, these models often generate outputs misaligned with human values, such as ethical norms and safety constraints, resulting in potentially harmful or inappropriate responses. While several techniques have been proposed to address this problem, they typically involve computationally intensive training procedures or introduce substantial inference-time latency. In this paper, we present DIESEL, a lightweight inference-guidance technique that can be seamlessly integrated into any autoregressive LLM to semantically filter undesirable content during generation. DIESEL guides generation by reranking token candidates according to their semantic similarity to predefined negative concepts in the latent space. It can serve either as a standalone safeguard or as an auxiliary defense layer, enhancing response safety without requiring model fine-tuning or additional data. We demonstrate DIESEL’s effectiveness on state-of-the-art conversational models, including in adversarial jailbreak scenarios. Furthermore, we show that DIESEL generalizes beyond safety applications, enabling flexible and domain-specific response filtering.