Why most n8n 'AI agents' are actually just advanced workflows with LLM integration

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1

I’ve been working with AI systems for years and I notice a big confusion in how people talk about agents built in n8n.

Don’t get me wrong - n8n is fantastic for automation. I use it constantly for building integrations and workflows. But calling every automation that includes ChatGPT or Claude an “AI agent” isn’t really accurate.

Here’s what I mean by actual agent types and how n8n handles them:

Simple Response Agents - These just take input and give output. Like chatbots that answer questions. n8n handles these perfectly since you’re just passing data to an LLM and returning results.

Planning Agents - These gather information from multiple sources before making decisions. You can build these in n8n by connecting various APIs and databases, but you have to design all the logic yourself.

Objective-Driven Agents - These work toward specific goals and adapt their approach. In n8n, you can store conversation state and use vector databases for context, but the goal-tracking logic is all manual.

Optimization Agents - These balance multiple factors to find the best solution. n8n lacks the mathematical tools for this - you need external ML systems.

Self-Improving Agents - These learn from past interactions. n8n can help collect training data and trigger model updates, but the actual learning happens elsewhere.

Real-Time Agents - These make instant decisions while following long-term plans. Workflow builders aren’t designed for this kind of split-second reasoning.

Collaborative Agent Networks - Multiple agents working together. n8n can coordinate some of this but you need message queues and distributed systems for true agent collaboration.

My typical setup uses n8n for orchestration plus:

  • PostgreSQL for storing agent state
  • Vector databases for semantic search
  • External planning frameworks
  • Message brokers for agent communication

The reality is n8n excels at orchestration but true autonomous agents need capabilities n8n doesn’t have built-in. You can create very useful agent-like systems, but they’re more like smart automations than independent agents.

What do you think - should n8n add native agent features or stick to being the best orchestration platform?

2

totally agree! people often hype up n8n too much. it’s great for workflows, but calling simple chatgpt uses “ai agents” is misleading. real agents should have more complex capabilities like planning and memory, and n8n isn’t there yet. just my two cents!

3

This resonates with my experience building what clients initially called “AI agents” but were really sophisticated decision trees with LLM endpoints. The marketing around “agents” has created unrealistic expectations about what workflow tools can deliver.

I think the issue runs deeper than just terminology though. Most businesses don’t actually need true autonomous agents - they need reliable, auditable processes that can handle complex logic and integrate multiple systems. n8n delivers this brilliantly, but calling it an agent platform sets wrong expectations.

The real problem comes when clients expect their n8n “agents” to learn and adapt independently. I’ve had to explain why their customer service automation can’t suddenly start handling completely new request types without manual workflow updates. That’s not a limitation - it’s actually a feature for production systems where you need predictable behavior.

From a practical standpoint, I’d rather see n8n improve its debugging and state management capabilities than try to become an agent platform. Better error handling, workflow versioning, and performance monitoring would make it even more valuable for building the orchestration layer that supports actual AI systems.

4

You’ve hit on something important here. I’ve been building automation systems for enterprise clients and see this terminology confusion constantly. The key distinction is autonomy versus orchestration.

What most people build in n8n are sophisticated workflow automations that happen to include LLM calls. They follow predetermined paths with some dynamic responses, but they’re not making independent decisions about how to approach problems.

I’ve found n8n works exceptionally well when you embrace its strengths rather than trying to force agent behavior. For instance, I built a customer support system that routes inquiries through multiple decision points, calls various APIs for context, and generates responses. It’s incredibly effective, but it’s still following my predefined logic tree.

The real limitation isn’t just technical - it’s architectural. True agents need to reason about their own processes and modify their behavior based on outcomes. n8n’s visual workflow paradigm doesn’t naturally support that kind of self-reflection.

Rather than adding agent features, I think n8n should focus on better integration with actual agent frameworks. Let it do what it does best - reliable orchestration and data transformation - while connecting seamlessly to systems designed for autonomous reasoning.