AI Agent Orchestration 2025: Multi-Agent Workflows for Production SEO Automation
Master multi-agent orchestration with sequential and parallel workflow patterns. Learn framework-agnostic approaches to building production AI systems that scale, with real examples from a 5-agent SEO content pipeline.
Single AI agents handle tasks. Multi-agent systems handle workflows. The difference? A single agent writes a blog post. A multi-agent system researches competitors, identifies content gaps, generates outlines, writes drafts, optimizes for SEO, and schedules publication—all while you sleep. By April 2025, production multi-agent systems are processing work that previously required entire teams. Our 91-agent system handles 2,847 SEO tasks daily—work equivalent to a 30-person content team. The breakthrough isn't more powerful models. It's orchestration: coordinating specialized agents into workflows that compound their capabilities. This guide teaches you how to orchestrate multi-agent workflows using framework-agnostic patterns that work whether you're using LangGraph, CrewAI, custom Python, or any other implementation. You'll learn the two essential orchestration patterns—sequential and parallel execution—through a real-world SEO content pipeline that transforms how content gets created. We cover agent coordination patterns, state management between agents, error handling strategies, and production deployment considerations including observability, cost management, and reliability patterns.
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