The best-kept secret about AI agents is that they often are not AI agents at all.
I see a lot of companies rushing headlong into AI agents, or executives who think AI agents are the solution to all their problems.
Spoiler: AI agents are not the right solution in the vast majority of cases
The idea is to cover the difference between a basic workflow, an AI workflow and an AI agent. And to share when AI agents actually make sense. In short, getting back on track to avoid mistakes that waste time (and money).
AI Workflow vs AI Agent
We all dream of an AI agent that works for us 24/7. But in reality, 99% of automations in production... are AI workflows. And that is no accident.
Companies want automation.
But they also want control, reliability, costs under control.
LLMs are not deterministic, your business needs to be
You see "AI agents" mentioned everywhere. And since I work with companies every week, I can tell you they are very rare.
Dig a little and you often find a simple workflow that follows a precise sequence of steps, with occasional calls to models.
And that is fine. Because the real autonomous AI agents, the ones able to make decisions, use the right tools and delegate to other agents... are still very unstable.
October 2026 update: what I was seeing in the field has since been given a name, "agent washing". In June 2025, Gartner used the term for AI assistants, RPA tools and chatbots rebranded as agents without the capabilities to match. By the research firm's estimate, only about 130 of the thousands of agentic AI vendors were the real thing.
The key difference? The degree of control you keep over execution.
Here is how to make sense of it:
1. Simple automation: Fully deterministic tasks, often via Make, Zapier or n8n.
Example: send an email to every new Stripe customer.
2. AI workflow: Automation + generative AI on one or more steps.
Example: extract the intent from an email with GPT, then categorize it in Notion.
3. AI agent: Autonomous, adaptive, unpredictable scenarios.
Example: read a brief, plan a response, ask for feedback, decide what to do, do it… without human help.
Why AI workflows still dominate
Because they allow:
- Controlled behavior (even with API calls to models, you constrain the output to a structured JSON format)
- Easy industrialization
- Clear, measurable costs
- Fast deployment to production
And above all: they avoid the lottery of fuzzy decisions made by a poorly supervised agent.
You can make a workflow smarter without turning it into an agent.
The automation tool family keeps growing
The conditional automation tools with the very deterministic flow we all know are super effective. They connect to thousands of apps so you can build automations in just a few clicks. Think Zapier, Make, n8n.
But they are limited when it comes to open, collaborative or evolving tasks. For example when the trigger depends on a user message, like on the support side.
This is how a new family emerges: AI agent orchestration tools such as Relevance AI, Lindy, Flowise, Gumloop, …
They handle:
- communication between agents
- multi-step decisions
- smart human-in-the-loop
- and dynamic missions with no fixed script
You move from “If A then B” to “Do whatever it takes to reach X”.
Here is an example where an agent delegates the task of creating a landing page to several agents before producing a first version to share:
The attempts by existing tools to adapt to this paradigm shift are complex, because it requires rethinking automation and the control interface…
But AI agents do have their place
In some cases, AI agents are completely relevant and they are the best solution. Often because the environment is so fast-changing that trying to anticipate everything to cover 100% of cases would be too complicated.
You would then end up with super heavy scenarios, impossible to maintain, like this one:
Some examples where agents really are a better fit:
- Text support agent: unpredictable input data, a writing style to follow, a tool needed to bring in the context to answer
- Web agent: free exploration, monitoring, smart scraping → more adaptive than a script with puppeteer
- Voice agent: voice conversation with follow-up and memory (not a rigid basic callbot)
In these cases, you accept uncertainty in exchange for a smooth, human experience, and you try to constrain the model through instructions and by adding conditional logic.
But it demands strong safeguards (logs, alerting, handoff, human-in-the-loop…).
AI workflow or AI agent, which one should you choose?
If you are just starting with AI and automation: begin with simple but well-designed AI workflows.
Avoid falling for the easy path by jumping straight into AI agents: it is the automation of automation → you feel like you no longer have to manage the workflow, just the instructions, but it goes off in all directions…
If you have use cases with high uncertainty, then explore AI agents, but carefully. Favor AI agent orchestration tools (or n8n) that are better suited to this kind of logic. On n8n specifically, that is the ground our n8n agency covers.
