AI request/response routing and orchestration platforms coordinate which models, agents, and services handle each user or system request, applying policies for cost, latency, capability and compliance. In practice these systems mediate model selection, chain multiple tools (retrieval, classifiers, external APIs), and manage multi‑agent workflows while exposing observability, governance, and human‑in‑the‑loop controls. Examples include specialist routers such as Orq.ai and developer frameworks or platforms like LangChain (developer‑first SDKs and deployment tooling), Kore.ai (enterprise multi‑agent orchestration with governance and observability), IBM watsonx Assistant (no‑code and developer paths for virtual agents), StackAI and Lindy (no‑/low‑code agent builders and governance), Yellow.ai (CX/EX agent automation across channels), and Adept (agentic automation that acts inside software interfaces). Why this matters in 2026: organizations increasingly run heterogeneous model fleets (open, closed, on‑prem and cloud) and assemble multi‑step automation spanning retrieval, reasoning, and external actions. Routing and orchestration platforms reduce operational complexity by enforcing policies (privacy, cost, latency, regulatory), providing audit trails and metrics, and enabling hybrid human/agent workflows. Key capabilities to evaluate include policy‑based model selection, request routing at scale, tool and API chaining, monitoring and observability, access controls and data governance, low‑code orchestration for business users, and developer SDKs for custom agents. As enterprises move from experimental pilots to production automation, these platforms bridge agent frameworks and workflow automation — enabling safer, more efficient use of LLMs and agentic systems while providing the controls required for enterprise deployment.