AI/LLM orchestration frameworks are the infrastructure layer that transforms isolated large language models into coordinated, production-ready agentic systems. These frameworks emerged to solve the fundamental challenge of building reliable multi-step workflows where AI agents must reason, plan, remember, delegate, recover from failures, and collaborate — capabilities that simple prompt-response patterns cannot provide. In 2026, the field consolidated around stateful graph-based architectures (LangGraph, Google ADK), multi-agent role systems (CrewAI, Microsoft Agent Framework), and type-safe validation patterns (Pydantic AI), each optimized for distinct production use cases. The critical shift is from "prompting LLMs" to programming agent systems — treating orchestration as a software engineering discipline with observability, error handling, state management, and deterministic control flow.