You read about AI agents everywhere: small digital team members that seem to be able to do magic - from answering customer emails to completely autonomous sales funnels. But do you really need an agent ecosystem for this or is a classic no-code workflow à la Make, Zapier or n8n enough?

Let's say you give your system this order: "Find all B2B leads for our new reporting tool, send them a personalized offer and book a demo slot in my calendar right away." An agent would:

1. Market & company data scraping
2. Research people
3. Writing emails and A/B testing tonality
4. Evaluate feedback and book appointments

A normal workflow could perform many of these steps, but only if you define each rule manually. This is where the wheat is separated from the chaff: agents learn on the go and adapt dynamically, while workflows reliably follow the same recipe. In this article, you'll find out what makes the two concepts tick, when to use which one - and why the future will most likely combine the two.

What exactly are AI agents?

At its core, an AI agent is a piece of software that pursues an overarching goal, perceives its environment, makes decisions independently and immediately incorporates what it has learned into its next action. There is no official definition of exactly where the line is drawn at which one speaks of an "agent"; however, the guiding principle is that it optimizes its goal itself, adapts to new information in real time and accesses tools such as browsers or databases as required without having to follow a hard-wired process.

The necessary intelligence is provided by large language models from OpenAI, Anthropic (Claude), Google or Mistral, for example. If you want to use them, you work via their APIs and need at least basic knowledge of prompt design, as these models generally come without a graphical user interface. This is exactly where modern automation platforms come in: Make has integrated its AI agents directly into the visual builder so that a natural-language target description is sufficient and the agent assembles the appropriate app modules at runtime. 

Zapier has been offering agents with an activity dashboard for monitoring since its redesign in May 2025 and groups specialized agents into pods. In all cases, you define the task in plain text, select which apps the agent is allowed to use and set guard rails; the agent then decides for itself whether it needs to scrape a website, query a database or write an email, for example, but documents every step so that you can intervene if necessary.

Also n8n also has its own AI agent node, based on LangChain. You drag it into the canvas, give your target a short order via prompt and activate the tools that the agent is allowed to access. During execution, it independently selects the appropriate tool, retains context in an internal memory and discloses each step in the logs so that you can intervene if necessary. This gives you a lean agent runtime environment within the same interface without additional infrastructure. As the internal memory only offers very limited storage space, you can alternatively integrate external sources such as databases. 

However, this much autonomy is not always necessary. When a process is clearly regulated, tightly controlled or dependent on absolute repeatability, classic no-code workflows come into their own - and this is exactly where we start in the next section.

What are no-code workflows?

No-code workflows are visual automations that work according to the trigger → action principle. Example: "When a new form submission comes in, create a line in Airtable and send a confirmation email." iPaaS tools such as Make, Zapier or n8n shine when it comes to data transfer between apps or repetitive tasks such as sending invoices.

Can you use AI in it? Sure, you can add a GPT module that summarizes the text of an email or performs a sentiment analysis, for example. But this is not yet an AI agent, because the model processes a clearly defined task without making any independent decisions afterwards. This is exactly how we at VisualMakers use our tool, the No-Code Navigator, to give you the best tech stack recommendation: An AI module is inside, but a workflow exists around it.

Advantages and disadvantages of the two

Use cases and examples

1. Lead Research & Outreach
   
   • Workflow: Import CSV, send e-mails with placeholders.
   • Agent: Searches for suitable companies, analyzes LinkedIn profiles, generates hyper-personalized emails, schedules follow-ups.
2. Support Inbox
   
   • Workflow: Ticket routing according to keywords.
   • Agent: Understands concerns, asks for context, solves 70 percent of cases himself, escalates the rest.
3. E-commerce pricing
   
   • Workflow: Preis-Update, wenn Lager < X.
   • Agent: Takes competitor prices, demand trends and margin into account, dynamically adjusts price within the limit.
4. IT Ops
   
   • Workflow: Send alert if CPU > 90 percent.
   • Agent: Increases container cluster, tests health checks, rolls out hotfix, documents change.

Challenges and risks

As powerful as AI agents are, they bring with them a whole new set of risks: Their models can hallucinate. A single false prompt is enough to issue incorrect invoices or disclose confidential data, which is why test environments, detailed protocols and human approval for critical steps are mandatory. Biased training data also harbors the risk of discriminatory decisions, which requires strict GDPR and ethics checks.

The maintenance effort also increases because, in addition to the usual API changes, agents need to be regularly provided with model updates, finely tuned prompts and solid monitoring. Those who do not cap their autonomy also risk an avalanche of costs because endless loops drive up token and server bills - fixed spending limits provide protection here. And last but not least, control is in danger of slipping away: The greater the scope for action, the more important clearly defined guard rails are, such as the rule that no orders over 500 euros may be triggered without approval.

Future trends

An entire ecosystem of new standards and building blocks is currently emerging around AI automation: Google is using the open agent-to-agent protocol A2A protocol, Google is aiming to enable independent bots to talk to each other directly, exchange data and coordinate tasks without having to reinvent every integration. At the same time Anthropics Model Context Protocol (MCP) - a kind of USB-C connector for AI - is spreading at the same time, allowing models from a wide range of providers to access external data silos and tools in a standardized way. For non-dev teams, visual construction kits such as Make AI Agents and the newly revised Zapier Agents lower the barrier to entry even further: enter the goal, select a few modules, test in the dashboard - done. 

Against this backdrop, leading researchers such as DeepMind CEO Demis Hassabis predict that by the mid-2030s we will have to reckon with so-called Artificial General Intelligence (AGI), an AI that no longer only performs specialized tasks, but can learn flexibly, reason and solve completely new problems in a similar way to a human being. This means that in the coming years, agents will take on more and more subtasks independently, and we need to develop the skills now to control them safely and responsibly. 

Conclusion

Workflows are your go-to when you:

• have clearly defined, repeatable processes
• Want transparency and predictability
• want to go live in hours instead of weeks
  

AI agents are worthwhile when:

• the environment is dynamic
• decision trees would grow exponentially
• personal context strongly influences the result

How much freedom you give your agents depends on the risk of the task: For low-stakes jobs such as testing newsletter subject lines, the bot is allowed to go off completely on its own - in the worst case, you just correct a trivial title. If, on the other hand, payment approvals, sensitive customer data or legal liabilities are involved, the human is forced to stay in the loop. The agent makes the suggestion, you check and approve. And when is it better to use classic workflows? Whenever the process is clearly defined, highly regulated or dependent on absolute repeat accuracy, because additional AI autonomy brings more risks than benefits.

Our tips for getting started:

→ 💪🏼 If you want to get started with no-code automation, take our free Make Fundamentals course!

→ 🦾 If you want to know how to set up an AI agent in n8n, we have an introduction to AI agents for you here!