AI Agent Orchestration Frameworks (blog.n8n.io)

This guide surveys the emerging category of AI agent orchestration frameworks and why teams are moving from monolithic, catch-all agents to networks of smaller, specialized agents that coordinate to complete complex workflows. Instead of pushing a single LLM to manage customer service, data analysis and scheduling simultaneously (which drives up token use and costs and hits practical complexity limits), orchestration frameworks let you compose domain-focused agents that share state, hand off tasks, and recover from failures — making multi-agent systems more reliable, efficient and maintainable in production. Technically, the guide argues orchestration frameworks need five core capabilities: persistent state/memory across agents, standardized communication protocols, flexible orchestration patterns (sequential, parallel, hierarchical), robust tool and API integration, and error-recovery/ retry logic. It reviews 11 platforms across three market segments: visual/low-code builders (n8n, Flowise, Zapier Agents) for business teams; code-first SDKs (LangGraph, CrewAI, OpenAI/Google/Microsoft SDKs) offering graph/stateful and human-in-the-loop control; and cloud-managed infrastructure (Amazon Bedrock Agents, Vertex AI Agent Builder, Azure AI Agent Service) that combine OSS SDKs with managed deployment. Notable technical features include graph-based state machines, RAG and vector DB support, MCP compatibility, multi-model hosting, and built-in debugging/eval tools — all signposting a shift toward modular, production-grade agent architectures.