What Multi-agent orchestration means in practice
Multi-agent orchestration runs a task as a coordinated cluster of specialised LLM agents rather than a single agent doing everything. A typical pattern: a planner agent decomposes the user goal into sub-tasks, executor agents handle each sub-task with their own tools, and a critic agent reviews the combined output. The architectural benefit is specialisation — the planner is prompted to think hierarchically, executors are prompted for narrow domains, and the critic is prompted to look for failure modes. The cost is latency and token spend. For enterprise use the pattern works best when the underlying task genuinely has parallelisable sub-problems (multi-document analysis, multi-system reconciliation); it under-performs when the task is a sequence of simple steps a single agent could handle.
Related terms
Agentic AI →
Systems where an LLM acts as a planner that chooses tools, decomposes tasks, and iterates toward a goal rather than producing a single completion.
Tool use (function calling) →
An LLM capability to emit a structured call to an external function or API based on the user's request, then continue the conversation with the function's result.
ReAct (Reason + Act) →
An agent design pattern where the LLM alternates between explicit reasoning steps ("thought") and tool-using action steps ("action"), looping until the goal is met.
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All 62 terms, in plain language
Sovereign AI, RAG, agentic AI, IDP, MLOps and the regulations that shape enterprise AI.