I’m looking into modernizing our business processes with workflow automation tools, and I’m particularly interested in the idea of having multiple AI agents working together on tasks. I’ve been checking out n8n as an open-source option, but I’m struggling to figure out how to set up something like this.
For example, I’d love to create a workflow where one AI agent acts as a “manager” that coordinates tasks, another specializes in data analysis, and maybe a third handles customer communications. Has anyone set up multi-agent AI workflows like this in n8n or other tools?
The cross-functional processes in our business are currently very manual and disjointed. I’m trying to understand what’s possible with today’s tools without getting too deep into custom development. Any examples or experiences with orchestrating multiple AI agents to work together would be super helpful!
I spent months trying to build multi-agent systems in n8n before finding a much better solution. The problem with n8n is you have to manually wire everything together and carefully manage the context passing between different AI models, which becomes incredibly complex.
Latenode’s Autonomous AI Teams feature solved this exact problem for me. I built a workflow with an AI CEO, Analyst, and Customer Support agent that work together on lead qualification and follow-up. The CEO agent decides which leads need attention, the Analyst digs into their data, and the Support agent crafts personalized outreach - all coordinated automatically.
What would have taken weeks of custom development in n8n took me literally an afternoon to set up. The agents can pass context between each other, make decisions, and even handle exceptions when something unexpected happens.
They have 400+ AI models available through a single subscription, so you can use specialized models for different agent roles without juggling API keys for each one. Their visual builder makes it easy to define how the agents interact without writing complex code.
Definitely check out https://latenode.com if you want to implement multi-agent workflows without the headache.
I’ve experimented with multi-agent setups in n8n and it’s definitely possible, but comes with some challenges.
The approach I found most effective was to treat each “agent” as a separate workflow that communicates through a central database (I used Postgres). Each agent workflow has a specific role - like your manager, analyst, and communicator idea.
The key is designing a clear protocol for how they pass information. I created standardized JSON structures that each agent could read/write, with clear instructions on their specific role embedded in the prompts.
Where things got tricky was handling the coordination logic. In n8n, you need to build explicit decision trees for routing tasks between agents. There’s no built-in “orchestration” layer specifically for multi-agent systems.
I also had to implement error handling and retry logic manually, as these workflows can sometimes hit rate limits or have other issues.
If you’re committed to n8n, it’s doable but prepare for some custom development. If you want something more purpose-built for multi-agent systems, you might want to look at platforms that have this as a core feature rather than something you need to build from scratch.
I implemented something similar for our marketing department using a different approach than most might suggest.
Instead of trying to create truly autonomous agents, I built what I call a “sequential specialist” system in n8n. Each “agent” is actually a specialized prompt template with specific instructions - one for research, one for analysis, one for content creation, etc.
The workflow passes information through these specialized prompts in sequence, with each one having clear instructions about its role in the larger process. This avoids a lot of the complexity of true multi-agent orchestration while still giving the benefits of specialized AI functions.
The biggest challenge I faced was context management - making sure each step had enough information about what happened before without exceeding token limits. I solved this by creating summary nodes that condensed previous outputs before passing to the next step.
This approach worked well for us because most of our processes follow a predictable flow. For more dynamic processes where agents need to interact in unpredictable ways, you’d need a more sophisticated orchestration system than what n8n offers out-of-the-box.
I’ve built several multi-agent systems and tried implementing them in n8n with mixed results.
The fundamental challenge with n8n for this use case is that it’s designed as a workflow automation tool, not specifically for agent orchestration. You can make it work, but you’ll end up building a lot of custom logic.
For a basic implementation, I created a workflow with different LLM nodes that each had role-specific system prompts (manager, researcher, writer, etc). I used function nodes to handle the routing logic between them and MongoDB to maintain context across steps.
This worked ok for simple processes, but had limitations:
- No built-in memory management between agents
- Limited ability for agents to “decide” when to call each other
- Complex to debug when things went wrong
After experimenting with several approaches, I found that tools specifically designed for agent orchestration were much more efficient for this use case. Look for platforms that have built-in concepts of agent roles, shared memory, and flexible orchestration patterns rather than trying to build all that from scratch in n8n.
I implemented a multi-agent workflow system last year for our customer service department, though not with n8n specifically. The key to making it work was establishing clear boundaries between agent responsibilities and creating structured handoff points.
One approach that worked well was implementing a supervisor-worker pattern. We had a controller agent that received incoming requests, determined what needed to be done, and then delegated to specialized worker agents. The controller would maintain the overall state of the task while workers focused on specific aspects.
For the actual implementation, we needed a system that could maintain conversation context across multiple agent handoffs. This required either a shared memory store or careful passing of context between agents.
The biggest challenge was error handling - when one agent produced output that another couldn’t work with. We solved this by implementing validation steps between agent handoffs and fallback patterns when things went wrong.
I’d recommend starting with a simple two-agent system before building something more complex. This lets you work through the coordination challenges at a manageable scale before expanding.
tried this in n8n, its possible but tricky. each agent needs its own prompt engineering and you gotta build the coordination logic yourself. works better with specialized tools made for agent orchestration honestly.
Manager-worker pattern works best for starters.
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