Table of Contents
Multi-agent systems — multiple specialised LLM agents collaborating — emerge as a 2026 production pattern for complex workflows. The orchestration patterns vary; pick by task structure.
Three patterns: supervisor (one agent delegates to specialists), peer collaboration (agents discuss / debate), role-based (CEO / CFO / engineer simulation). Most production: supervisor pattern with 2-5 specialists. Frameworks: AutoGen, CrewAI, LangGraph. Most teams: simpler is better — single-agent with tool use covers many use cases.
Patterns
- Supervisor: top-level agent decomposes task; delegates to specialised sub-agents (researcher, writer, fact-checker). Most production-friendly.
- Peer collaboration: agents discuss / debate; consensus emerges. Higher quality on hard problems; more compute.
- Role-based simulation: agents play different roles (analyst, devil's-advocate, executive). Useful for decision-support workflows.
- Sequential pipeline: each agent specialises in one stage; output flows through. Simplest; least adaptive.
Infrastructure
- AutoGen (Microsoft): mature multi-agent framework
- CrewAI: role-based crews
- LangGraph (LangChain): graph-based agent orchestration
- Custom: when you outgrow framework abstractions
- Self-hosted vLLM serves all agents with multi-LoRA per role
Verdict
Multi-agent systems are emerging in 2026 but most production AI doesn't need them. Single agent with tool use handles many tasks well. Supervisor pattern is the right default when multi-agent is justified. Don't adopt multi-agent complexity unless single-agent has measurably failed on your task.
Bottom line
Supervisor pattern when multi-agent justified. See smolagents.
