As generative AI (GenAI) applications continue to disrupt business operations, enterprises are recognizing the importance of deploying large language models (LLMs) with a focus on security, control, and transparency. Building effective LLM applications today demands advanced deployment strategies, supported by infrastructure tools like inference systems, guardrails, and observability frameworks. Here, we explore best practices for securely deploying and managing LLMs in enterprise environments, ensuring scalability, safety, and compliance.
Modular Deployment for Flexibility and Control
Deploying LLMs in enterprise settings often requires modular solutions that integrate high-performance inference systems with operational flexibility. Inference plays a pivotal role in enabling containerized deployments, allowing organizations to run models seamlessly across diverse environments — whether in the cloud or on-premises — while maintaining complete control over performance, latency, and security. This adaptability is crucial as enterprises balance operational efficiency with stringent requirements for data handling and compliance.
NVIDIA Inference Microservice (NIM) exemplifies this approach by providing a “model in a briefcase” setup. With NIM, enterprises can deploy LLMs in containerized formats, leveraging optimized inference to ensure low latency and high throughput. This modularity empowers organizations to tailor AI deployments to specific goals such as regulatory compliance or throughput optimization, while maintaining scalability and control over their endpoints.
Implementing Guardrails for Safe and Compliant AI
As AI models become increasingly sophisticated, they also need guardrails — customized rules and boundaries that define the limits of model behavior. Guardrails are customized rules and constraints that prevent models from producing undesirable outputs or veering off-topic, helping align AI behavior with organizational standards and regulatory requirements. These safeguards can restrict sensitive responses, block problematic topics, or address unusual patterns, mitigating risks such as data leaks or non-compliance. For instance, NVIDIA NeMo Guardrails provides a framework for creating tailored rules that govern AI behavior, enabling companies to maintain safety and compliance. Built on a dialogue modeling language called Colang, it facilitates nuanced, situation-specific constraints to prevent inappropriate interactions, such as off-topic conversations or the exposure of sensitive data.
A well-constructed guardrail system not only boosts compliance but also instills trust in AI applications. By carefully managing what models can and cannot do, companies can deploy AI in public-facing roles with greater confidence.
From Retrieval-Augmented Generation to Agentic AI Systems
Retrieval-Augmented Generation (RAG) enhances AI accuracy by grounding responses in external, verified data, reducing hallucinations and ensuring relevance. Instead of relying solely on pre-trained knowledge, RAG enables AI to query trusted sources for up-to-date information, such as retrieving real-time data for user queries. This approach has evolved into agentic systems, where multiple specialized AI agents collaborate to handle complex tasks. Each agent focuses on a specific function — like retrieving information, performing calculations, or generating responses — creating a modular, adaptable AI ecosystem.
Agentic systems allow enterprises to efficiently address multifaceted user needs by dynamically routing tasks to the appropriate agents, supported by inference systems that ensure seamless integration and execution. For instance, a chatbot might rely on an inference engine to process user inputs in real-time, query one agent for product details, and another for delivery estimates, all while maintaining high performance and accuracy. These inference-powered workflows enhance the reliability and scalability of agentic systems.
With applications ranging from customer support to healthcare, finance, and legal research, agentic AI systems are unlocking new possibilities for intelligent automation. By mimicking human-like collaboration between agents, these systems provide more responsive and reliable solutions, while their modular nature ensures transparency and simplifies monitoring. As enterprises adopt this framework, agentic systems offer a flexible and future-ready approach to deploying AI at scale.
Observability: Enabling Transparency and Monitoring in AI
As AI becomes integral to business operations, observability is key to ensuring transparency, accountability, and high performance. Observability tools like Fiddler empower enterprises to monitor, analyze, and improve LLM applications in production, driving trust and transparency in LLM applications.
At the core of Fiddler’s LLM monitoring capability is the Fiddler Trust Service, powered by proprietary, fine-tuned Fiddler Trust Models. These models deliver high-accuracy scoring of LLM prompts and responses with low latency, scale seamlessly to handle growing traffic across diverse deployment environments (including air-gapped systems), and provide cost-effective, real-time guardrails alongside offline diagnostics. They detect and address critical issues such as hallucinations, toxicity, PII leakage, and prompt injection attacks.
For companies deploying multi-agent or complex retrieval-augmented systems, observability provides a clear lens into how AI agents communicate and interact. By offering actionable insights into these dynamic, collaborative systems, Fiddler helps businesses monitor, refine, and adapt AI-driven workflows for optimal outcomes.
Addressing Ethical, Regulatory, and Governance Considerations
Ethics, regulations, and governance are critical and ever-evolving requirements that responsible LLM applications must meet. Enterprises deploying LLM applications need to comply with stringent data privacy laws, industry-specific safety standards, and governance, risk, and compliance (GRC) frameworks to mitigate risks and meet regulatory expectations. Observability tools like Fiddler not only help monitor LLM performance but also provide the detailed logs and audit evidence required for regulatory compliance. These insights enable enterprises to demonstrate accountability and maintain a transparent record of LLM application interactions.
By integrating observability into GRC-aligned workflows, companies can establish robust policies for data handling, mitigate biases, and ensure fair, secure, and lawful decision-making processes. This alignment not only helps organizations comply with evolving AI regulations but also demonstrates their commitment to ethical AI.
With proper ethical frameworks alongside GRC-aligned processes, enterprises can foster trust in their LLM applications while safeguarding against reputational, legal, and operational risks. Whether through modular applications that isolate sensitive processes or agentic architectures that allow for specialized compliance agents, organizations can ensure their LLM deployments meet social, regulatory, and governance standards while remaining scalable and efficient.
Conclusion: Deploying Enterprise-Level LLM Applications
As LLM applications grow in complexity and impact, enterprises require deployment strategies that balance scalability, compliance, and innovation. These modular components — advanced inference systems for real-time optimization, robust guardrails for safety, and observability tools for compliance — create a unified framework that ensures LLM applications operate reliably and ethically at scale.
These tools collectively empower enterprises to harness the transformative potential of AI across diverse industries — from customer service to knowledge management — while fostering trust, driving innovation, and ensuring long-term success. With this unified approach, enterprises can unlock the full potential of LLM applications while maintaining the accountability and adaptability required in today’s dynamic landscape.
Watch the full AI Explained: Inference, Guardrails, and Observability for LLMs
