Multi‑agent AI orchestration platforms provide the infrastructure to compose, run and monitor systems made from multiple specialized AI “agents” rather than a single general model. This topic covers engineering frameworks (LangChain, AutoGen-style stacks), developer-focused environments (Warp, Blackbox.ai), no‑code/low‑code visual designers (MindStudio, AgentGPT), and supporting services for retrieval, data, and model lifecycle (LlamaIndex, OpenPipe, Continue). Fujitsu’s multi‑AI agent initiatives illustrate how vendors are packaging multi‑agent patterns for enterprise use cases such as process automation and cross‑system coordination. Relevance in 2026 stems from three converging trends: a shift toward modular agent architectures that combine retrieval, planning, and tool use; rising demand for observable, stateful orchestration (e.g., LangChain’s LangGraph patterns); and broader enterprise needs for low‑code deployment, governance, and continuous evaluation. Tools like LangChain offer developer primitives for building, debugging, and deploying agentic apps; Warp and Blackbox.ai embed agents into developer workflows; MindStudio and AgentGPT lower the barrier for non‑engineering teams to design and test agents; LlamaIndex and OpenPipe focus on data plumbing, RAG, and model tuning; Continue enables “continuous AI” automation across interfaces. Key practical concerns are reliability, cost/latency tradeoffs, data governance, and evaluation/safety tooling. As organizations move from prototypes to production, orchestration platforms are consolidating features for state management, observability, and enterprise controls. Understanding the roles of agent frameworks, ADEs, low‑code platforms, and supporting data/model services is essential for selecting an orchestration approach that fits technical constraints and compliance requirements.