Multi-Agent Systems in n8n
Coordinate specialist agents using n8n. Planner + worker patterns and handoffs.
Key takeaways
- To coordinate specialist agents in n8n, split the workflow into: retrieve, decide, act, and evaluate.
- Structure your prompts with a system frame, task frame, and output frame — every time.
- Validate every LLM output against a JSON schema before it enters your business logic.
- Log tokens, latency, and cost per execution so you can optimize what matters.
AI workflows in n8n go beyond calling a single LLM. To ship reliably you need to coordinate specialist agents — and this guide covers exactly how, with planner + worker patterns and handoffs that hold up in production. Expect concrete node choices, prompt templates, and evaluation loops, not marketing copy.
Architecture
A production AI workflow in n8n has four layers: input normalization, retrieval, model call, and post-processing. Each layer is a sub-workflow you can test in isolation. To coordinate specialist agents, wire the retrieval layer to a vector store (Pinecone, Qdrant, or PGVector) and the model layer to a provider you can swap.
Keep prompts in files or a database, not hard-coded in a Set node. That way you can A/B-test prompts without editing the workflow.
- Use the AI Agent node for tool use; use plain LLM nodes for pure generation.
- Cache identical prompts to cut cost by 30-60%.
- Route to a smaller model when the task is easy — save the big model for the hard ones.
Prompting and structured output
For coordinate specialist agents, ask the model for JSON that fits a strict schema. Use OpenAI's json_schema mode, Claude's tool-use hack, or Gemini's response schema. Validate the parsed output before touching downstream systems; on failure, retry with a repair prompt.
Never trust a single generation. Add a critic step for high-stakes outputs and a human review queue for anything irreversible.
Evaluation
Ship an eval loop with every AI workflow. Keep a golden set of 20-100 inputs with expected outputs. Run the eval on every prompt or model change and gate deploys on it. When you coordinate specialist agents, evals are the difference between a demo and a production system.
Track four numbers per version: pass rate, average latency, average cost, and hallucination rate. Regressions on any one are a blocker.
Frequently asked questions
- Which LLM should I use?
- Start with a mid-tier model (GPT-4o-mini, Claude Haiku, Gemini Flash) for iteration. Escalate to premium models only where evals prove it pays.
- How do I keep AI cost predictable?
- Cache prompts, cap max tokens, and route easy requests to smaller models. Log cost per workflow and alert on outliers.
- Do I need a vector database?
- Only for retrieval. PGVector inside Postgres is the cheapest starting point; move to Pinecone or Qdrant when you exceed a few million vectors.
- How do I test AI workflows?
- Golden sets + LLM-as-judge + human spot-check. Run on every deploy.