N8N vs. Claude Code vs. Hermes: Which Level of Agentic AI Do You Actually Need?
There are 4 levels of agentic AI — from Zapier chatbots to full agentic OS. Here's how to know which level fits your workflow before you build the wrong thing.
You’re Probably Building at the Wrong Level
N8N, Claude Code, and Hermes are not competing products. They solve different problems at different levels of complexity, and picking the wrong one doesn’t just waste your weekend — it wastes the next six months of iteration on something that was never going to work the way you needed it to.
The four-level framework that’s emerged from practitioners building real agentic systems gives you a clean way to diagnose this before you build: Level 1 Chatbots → Level 2 AI Workflows (N8N/Zapier) → Level 3 Agentic Workflows (Claude Code/Codex/Cursor) → Level 4 Agentic AI Systems. Each level gives the AI more autonomy. Each level also requires more from you in terms of setup, maintenance, and understanding of what’s actually happening inside the system.
Most people are building at Level 2 when they need Level 3, or attempting Level 4 when Level 3 would serve them fine for another year. Both mistakes are expensive.
This post is a map. Read it before you commit to a stack.
The Dimensions That Actually Separate These Levels
Before comparing tools, you need the right criteria. The wrong criteria — “which one is most powerful?” or “which one has the best UI?” — will lead you to the wrong answer.
Who decides the execution path. This is the single most important dimension. At Level 2, you define every step. The workflow runs those steps in order, every time, regardless of what the output looks like. At Level 3 and above, the model decides the steps based on the goal you give it. That shift has enormous practical consequences for what you can and can’t build.
Whether the system adapts between runs. A Level 2 N8N workflow has your prompt templates hardcoded from three months ago. If your best-performing LinkedIn posts this month are carousels instead of text posts, the workflow doesn’t know that. It keeps doing what you told it to do when you built it. Level 4 systems carry memory between sessions — which posts performed, which subject lines got opens, what the model learned last week.
How much scaffolding you’re responsible for. At Level 1, you’re responsible for nothing except the prompt. At Level 4, you’re responsible for skills (markdown instruction files), memory architecture, MCP connections to live data, hooks and scripts for deterministic validation, and the coordination logic between multiple agents. The scaffolding is where most of the real work lives — and most of the real leverage.
Whether a human is in the loop by design. Every level that actually works in production has deliberate human checkpoints. The question is where. At Level 2, you review before publishing. At Level 4, the system does most of the quality checking itself and surfaces only the exceptions. “Set it and forget it” is not a design principle that works — the systems that run reliably have human review built into specific stages.
Team distribution. Can someone else on your team use this without you reconstructing the setup for them? A prompt can’t really travel. A skill (a markdown file with YAML front matter describing the use case) can. A plugin — which bundles skills, app integrations, MCP servers, hooks, assets, and commands — can be installed by anyone on the team without them understanding how it was built.
Level 1 and Level 2: Where Most People Actually Are
Level 1 is ChatGPT or Claude in a browser tab. You paste a transcript, you ask for a LinkedIn post, you get something that reads like a LinkedIn post with too many emojis. It doesn’t know your audience, your voice, or what you posted last week. It’s passive — it waits for you to prompt it, and it doesn’t execute anything on its own.
This is where most people start, and there’s nothing wrong with it for genuinely one-off tasks. A prompt is the right tool when the task is temporary, small, and specific to the moment. The problem is that most people stay here too long, piling more and more into their prompts to compensate for the lack of structure, and generating hours of wasted effort every week as a result.
Level 2 is N8N, Zapier, Make.com. You build a pipeline: trigger fires when a new YouTube video publishes, transcript gets pulled, Claude gets called through an AI node with your voice guidelines hardcoded into the prompt, draft drops into your scheduling tool. For someone coming from Level 1, this feels like magic. The workflow runs on repeat without you touching it.
Remy doesn't build the plumbing. It inherits it.
Other agents wire up auth, databases, models, and integrations from scratch every time you ask them to build something.
Remy ships with all of it from MindStudio — so every cycle goes into the app you actually want.
The limitation is that the workflow can’t think. If the video topic doesn’t suit LinkedIn at all and would work better as an X thread, the workflow doesn’t make that call. It runs the same steps in the same order regardless. If the output quality degrades, you go back and rewrite the prompt yourself. The AI is doing some of the work, but you’ve defined every step, and the system is only as smart as those steps were when you wrote them.
N8N is the right tool when your process is genuinely linear, the steps don’t need to vary based on content, and you want reliable automation without model decision-making in the middle. It’s also the right tool when your team needs something they can maintain without understanding agent architecture. For a lot of business workflows — weekly reports, data syncing, notification routing — Level 2 is exactly sufficient and Level 3 would be overkill.
Level 3: The ReAct Loop and What “Harness” Actually Means
Level 3 is where the model starts deciding the execution path. You open Claude Code and say “turn this week’s video into content for LinkedIn, Twitter, and my newsletter.” Claude Code pulls the transcript, reads your brand voice file, looks at the video topic, decides which moments work best for which platform, drafts a LinkedIn carousel because the topic suits visual storytelling, writes an X thread because there’s a contrarian angle, runs everything through your style guide, rewrites what doesn’t pass, and saves it for review.
You didn’t write those steps. The model decided them based on the goal.
The technical name for this is the ReAct loop — Reason and Act. The model reasons about what to do, acts on it, observes the result, and iterates until it’s done. Claude Code, OpenAI Codex, and Cursor are all implementations of the same idea: a harness. A harness is the infrastructure that surrounds the model to make it reliable and deployable for actual work. Without a harness, you have a chatbot in a browser tab. With a harness, the model can read your files, run commands, call other tools, and check its own work.
Claude Code is a harness. Codex is a harness. Cursor is a harness. Different products, same concept. They wrap around the model and give it the ability to act on your computer, with your files, using your tools. OpenAI describes GPT-5.5 as “better at messy multi-part work like planning, using tools, checking its work, and navigating ambiguity” — that description is really a description of what a harness enables, not just what the model does on its own.
If you want to understand the range of patterns available at this level, the five Claude Code workflow patterns cover schema migrations, test loops, and other real engineering tasks that illustrate what the ReAct loop looks like in practice.
Level 3 tops out at one agent, one goal, one terminal session. It doesn’t remember what it learned last week. If you want it to also extract video clips, build carousels, generate ad copy, and schedule everything, you run each task separately and re-explain context every time. That’s the ceiling.
For most teams, Level 3 is the right answer right now. The jump from Level 2 to Level 3 is significant and delivers real time savings. The jump from Level 3 to Level 4 is also significant, but requires substantially more investment in system design.
Level 4: Agentic AI Systems and What Hermes Actually Is
Remy doesn't write the code. It manages the agents who do.
Remy runs the project. The specialists do the work. You work with the PM, not the implementers.
Level 4 is a coordinated system running your operations. Not one agent on one task — multiple agents with their own instructions, quality bars, and output formats, coordinated by a system that loads context at the right time and surfaces exceptions for human review.
The content repurposing example at Level 4 looks like this: one trigger command fires a full content engine. One skill extracts the best clips from the video and ranks them. Another builds platform-specific carousels with the right dimensions and copy. Another drafts the weekly newsletter from key takeaways. Another generates ad copy from angles that have historically performed. Everything queues into the scheduling tool. The system checks its own work, flags anything that needs a human, and handles the rest.
The tools at this level are things like Hermes and OpenClaw — open-source personal agent frameworks that have grown significantly in adoption. Both take the same approach: build a richer system of files on top of a base agent so it can handle real complexity, feeding context at the right time, with a human in the seat at the right checkpoints.
The building blocks are simpler than they sound. Skills are markdown files — your brand voice, quality rules, platform-specific instructions. The agent loads the right skill only when it needs it, which keeps context lean and token costs manageable. MCP (Model Context Protocol) connects the system to live data — your scheduling platform, analytics dashboard, CRM. Memory can be as simple as a markdown file the system reads and updates between sessions, or as complex as a database that persists context across all your tools.
This is also where the distinction between a plugin and an MCP connector matters. An MCP is a universal plug to live data — you put it in, you get data back out. A plugin is a larger package that can contain an MCP, but also contains skills, hooks, scripts, and metadata. The plugin is the whole workflow in a neat installable unit. The MCP is one component inside it. Confusing these two is one of the most common mistakes people make when designing Level 4 systems.
For teams building at this level, understanding the three-layer memory architecture that Claude Code uses internally is worth the time — it informs how you design your own memory system and where to put what.
Platforms like MindStudio handle a lot of the orchestration infrastructure at this level: 200+ models, 1,000+ integrations, and a visual builder for chaining agents and workflows — which matters when you’re coordinating multiple agents that each need different model characteristics and different tool access.
The honest caveat on Level 4: the systems that actually work in production have deliberate human review built in at specific stages. The AI does the heavy lifting — drafting, checking, formatting — but nothing goes live without a human seeing it first. “95% autonomous” is achievable. “100% autonomous” is not a design principle that works yet.
Which Level Fits Your Situation
Hire a contractor. Not another power tool.
Cursor, Bolt, Lovable, v0 are tools. You still run the project.
With Remy, the project runs itself.
Use Level 2 (N8N/Zapier) if: your process is genuinely linear and the steps don’t need to vary. You’re routing data, triggering notifications, running the same transformation on every input. Your team needs to maintain this without understanding agent architecture. You want reliability over adaptability. A weekly report that always pulls the same sources and formats them the same way is a Level 2 workflow, and making it Level 3 adds complexity without adding value.
Use Level 3 (Claude Code/Codex/Cursor) if: the execution path needs to vary based on content. You’re doing work where judgment matters — which platform suits this topic, which angle is strongest, which parts of the codebase need to change. You want the model deciding the steps, not you pre-defining them. Most knowledge workers doing content, research, or code work will find Level 3 covers 80% of what they actually need. The social media content repurposing skill for Claude Code is a concrete example of what Level 3 looks like when it’s working well.
Use Level 4 (Hermes/OpenClaw/custom agentic OS) if: you’re running an operation, not a task. Multiple parallel workstreams that need to coordinate. Memory that needs to persist between sessions and inform future runs. Quality bars that vary by output type. A team that needs the system to handle complexity autonomously and surface only the exceptions. This is the right level when Level 3 keeps hitting its ceiling — you’re re-explaining context every session, running tasks sequentially that could run in parallel, or losing learned context between runs.
One thing worth flagging: the jump from Level 3 to Level 4 is not primarily a technical jump. It’s a system design jump. You need to know how to draw edges around a workflow — what’s one plugin versus three, what belongs in a skill versus a hook, what should be deterministic and never left to the model. That skill is genuinely valuable and genuinely rare. If you’re building toward Level 4, the WAT framework (Workflows, Agents, Tools) is a useful structure for thinking about how to decompose your system before you start building it.
For teams building Level 4 systems that need persistent memory across sessions, building a self-evolving memory system with hooks covers one practical approach — using Claude Code hooks to capture session logs and extract lessons automatically.
If the work you’re doing is building a full application rather than orchestrating agents, the abstraction question shifts. Tools like Remy take a different approach: you write a spec — annotated markdown where prose carries intent and annotations carry precision — and the full-stack app gets compiled from it. TypeScript backend, SQLite database, auth, deployment. The spec is the source of truth; the code is derived output. That’s a different level of abstraction than agent orchestration, but it’s worth knowing it exists when the question is “how do I go from this workflow to a deployed product.”
The Real Question
The four-level framework is useful not because it tells you which tool is best, but because it tells you what you’re actually asking the system to do. Most people who are frustrated with their AI workflows are frustrated because they’re asking a Level 2 system to make Level 3 decisions, or they’re building Level 4 complexity for a workflow that would work fine as a skill in a Level 3 harness.
One coffee. One working app.
You bring the idea. Remy manages the project.
The presenter in the source material puts it plainly: if you copy from one app, paste into chat, ask the model to reason, go get data from somewhere else, and check the result — you are the human plugin. The question is whether you want to keep doing that manually or encode it into something that runs without you.
The answer to that question determines your level. The level determines your tools. Pick in that order.
