AI Guardrails in .NET: Securing RAG, Agents, and Tool Calling As AI applications move beyond simple chat interfaces into RAG pipelines, AI agents, tool calling, and automated workflows , one architectural problem becomes much harder to ignore: How do you make sure the AI stays within the boundaries of your application? An LLM can summarize a document, classify information, generate code, answer questions, call tools, and interact with external systems. But the model is still a probabilistic component. It can misunderstand a request, follow malicious instructions hidden inside retrieved content, generate an unsafe response, expose information that the user should not see, or propose an action that the application should never execute automatically. This is where AI guardrails become important. A guardrail is an application control that can validate, restrict, authorize, monitor, or block an AI input, output, context, tool call, or action. That definition matters be...