Single AI agents have limits. They handle straightforward tasks well but struggle with complex workflows requiring different types of expertise. Multi-agent orchestration solves this by dividing work among specialized agents working in coordination.

How Multi-Agent Systems Work

An orchestrator receives a complex task and breaks it into subtasks. Each subtask goes to a specialized agent: one handles research, another writes content, a third optimizes for SEO, a fourth generates images. The orchestrator manages handoffs, resolves conflicts, and assembles the final output.

Why Specialization Beats Generalization

A single agent tries to do everything with one model. Multi-agent systems use the right tool for each job. A research agent optimizes for information gathering. A writing agent tunes for brand voice. Specialization improves quality across complex workflows that would overwhelm a generalist agent.

Applications in Marketing

Marketing tasks are multi-disciplinary. Creating a landing page requires research, copywriting, design, SEO, and implementation. Multi-agent orchestration handles this end-to-end, with each agent contributing expertise while the orchestrator ensures coherent results.