Large Language Models (LLMs) have achieved strong performance across diverse NLP tasks, but deploying them in real-world scenarios raises concerns regarding safety, reliability, and computational cost. To mitigate the risk of harmful, unreliable, or unfaithful content, LLM pipelines increasingly rely on guardrails. Yet deploying multiple guardrails across different tasks and risk categories introduces significant computational and orchestration overhead. In this work, we propose a calibrated deferral-routing framework for deploying guardrails both accurately and cost-effectively. Our approach arranges guardrails as a cascade of increasing capability and cost: each input is first evaluated by a lightweight guardrail, while only uncertain instances are deferred to a larger, more capable, and more expensive model. We formalize this routing problem and study practical deferral-calibration strategies by applying post-hoc calibration, including isotonic regression, Platt scaling, and temperature scaling. We evaluate 18 guardrail models from four model families (i.e., Granite Guardian, Llama Guard, ShieldGemma, and GuardReasoner) across 10 harm-detection benchmarks. Our experiments show that: (i) all guardrail models are often poorly calibrated out of the box (ECE ranging from 6% to 28%); (ii) post-hoc calibration substantially improves reliability reducing ECE below 2.5% in most cases; (iii) cascade routing yields large cost savings with minimal accuracy loss, while monotone calibration primarily makes routing thresholds deployable rather than improving rank ordering.

The deployment of language models in real-world applications exposes users to various risks, including hallucinations and harmful or unethical content. These challenges highlight the urgent need for robust safeguards to ensure safe and responsible AI. To address this, we introduce Granite Guardian, a suite of advanced models designed to detect and mitigate risks associated with prompts and responses, enabling seamless integration with any large language model (LLM). Unlike existing open-source solutions, our Granite Guardian models provide comprehensive coverage across a wide range of risk dimensions, including social bias, profanity, violence, sexual content, unethical behavior, jailbreaking, and hallucination-related issues such as context relevance, groundedness, and answer accuracy in retrieval-augmented generation (RAG) scenarios. Trained on a unique dataset combining diverse human annotations and synthetic data, Granite Guardian excels in identifying risks often overlooked by traditional detection systems, particularly jailbreak attempts and RAG-specific challenges. https://github.com/ibm-granite/granite-guardian

The advent of Large Language Models (LLMs) has transformed how complex tasks across various domains can be automated. One of the industry trends today is Agentic AI, which leverages LLMs to operate multiple tools and provide automatic configuration.In the domain of cloud computing, Agentic AI might be used, for example, with the generation of Kubernetes manifests – structured configuration files that define containerized environments.However, effectively applying LLMs to domain-specific tasks often reveals knowledge gaps that impact the accuracy and reliability of the generated output.To address these challenges, we propose KGen, a pipeline for generating K8s manifests directly from user-described intents expressed in natural language using LLMs. Our approach leverages an extensive n-shot learning analysis to choose the appropriate number of examples that can better guide the adopted models in generating the manuscripts while also looking at the computational cost. Our results validate the use of LLM in this task and show that (as expected) increasing the number of n-shot examples can improve the quality of the generated configurations when adopting more specialized models, such as Mixtral-8x7B (which uses the Mixture of Experts approach) and Prometheus-8x7B-v2.0, but (surprisingly) for more general-purpose models like Llama3-8B and Llama3-70B, it can lead to smaller number of valid K8s manifests. These results underscore the complexities of adapting LLMs for domain-specific structured generation and emphasize the need for an in-depth analysis to determine the most effective setup, also suggesting that smaller models sometimes outperform their larger counterparts for each domain-specific task.