# How Do Companies Build Their Own AI? A Look Inside Enterprise AI Architecture DevFeed: [How Do Companies Build Their Own AI? A Look Inside Enterprise AI Architecture]() Original publisher: [Read original article]() Author: Geeta Kakrani Published: 2026-11-13T14:23:08Z Content type: article Language: en Sources: [Google Developer Experts]() Topics: [Artificial Intelligence](), [LLM security](), [ai security](), [Retrieval Augmented Generation (RAG)](), [ai observability](), [LLM observability](), [observability](), [Model Routing](), [Large Language Model](), [AI Chat]() Tags: [agent-guardrails](), [ai](), [ai-agent](), [ai-architecture](), [architecture](), [enterprise](), [enterprise-ai](), [enterprise-ai-architecture](), [model-context-protocol](), [rags]() ## AI overview Production enterprise AI requires multiple engineering layers beyond an LLM and chat interface. The article explains using RAG to ground answers in company documents, and tool calling through MCP to let agents interact with HR, Jira, Slack and other systems. Authentication and RBAC must enforce each user's permissions at the backend; guardrails such as injection detection and PII filtering add protections but do not replace authorization. Semantic caching and model routing can reduce cost and latency, with care for permissions, freshness and quality. Observability tracks retrieval, model calls, tools and execution traces; evaluations test accuracy, faithfulness, retrieval, tool use, security, cost and regressions after changes. Together these layers support safer, more reliable production systems. ## Source excerpt Building an AI agent is easy. Making it reliable, secure, cost-efficient, and ready for thousands of employees is the real engineering challenge.Imagine your company has deployed an AI assistant for all its employees. An employee opens the chat interface and asks: "What is our company's leave policy?" The AI responds confidently: "You get 30 paid leaves and unlimited sick leave." The answer sounds professional. The employee trusts it and plans their vacation accordingly. But there is a problem. Your company never defined this policy. The AI simply invented the answer. This is called hallucination, and it is one of the biggest challenges when deploying Large Language Models (LLMs) in real business environments. Now imagine another situation. Your company has connected this AI assistant to its HR system, Jira, Slack, and internal databases. Employees can ask questions, apply for leave, raise IT tickets, and access company information. It looks impressive. But what happens when an intern asks for the CTO's or Senior Manager's salary? What happens when someone tries to manipulate the agent into revealing confidential information? What happens when 10,000 employees start using it every day and your API bill increases dramatically? These are not theoretical problems. They are real engineering challenges that appear when we move from AI prototypes to production systems. Building an enterprise AI agent is not just about selecting a powerful model and connecting it to a chatbot interface. It requires multiple layers of engineering. Let's understand these layers through a practical example: building an AI assistant for a company. 1. RAG: Stop Your AI from Inventing Company Policies The first problem is knowledge. An LLM has been trained on large amounts of publicly available information, but it does not automatically know your company's internal documents. It does not know: Your employee leave policy. Your work-from-home guidelines. Your reimbursement process. Your internal I