Secure AI deployment platforms for classified and regulated networks focus on enabling AI agents and assistants to run where data sensitivity, auditability, and legal controls are primary constraints. This topic covers infrastructure and governance patterns needed to deploy agentic AI in air‑gapped or tightly controlled environments, and the tooling used to build, operate, and demonstrate compliance. Key considerations include strong isolation and approved cryptography, hardware-backed key management, model provenance and validation, runtime monitoring and behavior controls, human‑in‑the‑loop gating, and detailed audit trails for regulatory review. Emerging operational patterns pair enterprise-grade AI infrastructure that provides deep visibility into agent activity with no‑code/low‑code platforms that accelerate agent development while enforcing policies. Representative tools illustrate the stack: Xilos positions itself as an “intelligent agentic AI infrastructure” offering enterprise visibility into connected services and agent actions; StackAI and Lindy provide end‑to‑end no‑/low‑code platforms to build, deploy, and govern autonomous agents; IBM watsonx Assistant targets enterprise virtual assistants and multi‑agent orchestrations with no‑code and developer workflows; Anthropic’s Claude family supplies conversational and developer assistants that can be governed as part of a secure deployment; and Anakin.ai offers a library of ready apps for automation and content tasks that can be integrated under governance controls. By 2026 the combination of stricter regulatory expectations, broader adoption of agentic systems, and more mature governance tooling makes it practical to run advanced AI inside regulated enclaves — but success depends on choosing infrastructure and platforms that prioritize verifiable visibility, policy enforcement, and demonstrable compliance over convenience alone.