AgentOS vs Make: a canvas you build on, or a workflow you describe?

Updated October 7, 2026 · by Ashtin Dowler · 8 min read

Make gives you a visual canvas for building automations and AI agents. AgentOS lets you describe the workflow and runs it on what your company already knows. In Make you lay out a scenario step by step: connect modules, map the data between them, add routers for the branches. It's flexible and it's priced fairly. In AgentOS you connect your tools once and say what you want done. If someone on your team enjoys building automations, Make is a great tool. If the people who need the workflow would never open a canvas, AgentOS is the shorter path.

AgentOS vs Make at a glance

	Make	AgentOS
What it is	A visual platform for building automations and AI agents, with 3,000+ app connections	A workspace that holds your company's knowledge and tools, runs workflows on them, and shares them with every AI tool you use
Building a workflow	Lay out a scenario on a canvas: connect modules, map data between them, add routers for branches. An assistant called Maia can draft one from a prompt	Describe it in plain language. Otto, the built-in agent, builds it from your connected tools, runs it on a schedule and checks in when a decision needs you
Who it's for	People who like building: operations specialists, marketers and developers	Operations, sales and finance teams, with no building required
Company knowledge	You give each agent its own instructions and knowledge files, such as policies and FAQs	Already there. Every workflow runs on the same shared knowledge of your business
Approval before changes	You can add manual approval steps or stop points to an agent	Important changes to your business systems wait for a person to approve them
Models	Make's own AI provider on every plan. Connect your own provider, such as OpenAI or Claude, on paid plans	Model-agnostic. Use any model side by side, or bring your own key
Seeing what AI costs	Credits and usage inside Make. With your own provider, the model bill arrives separately	One view of AI use and spend across the tools and teams you connect
Pricing	Free plan with 1,000 credits a month. Paid plans listed from $12 a month for 10,000 credits (as of Oct 7, 2026)	Free to start. See the pricing page for current plans

Make details come from make.com and Make's help center, checked October 7, 2026. Make moved from operations to credits and is still rolling out its AI agents, so check the sources below before you buy.

What does Make do well?

Make is one of the best visual automation tools there is. You can see the whole process on one canvas, which makes a complicated flow easier to follow than a long list of steps. Routers let a scenario branch in several directions, and the library is huge: Make lists more than 3,000 app connections and more than 400 for AI tools.

It has kept up with AI, too. Make AI Agents can be given instructions, knowledge files and tools, and you can watch an agent's reasoning step by step. Maia, Make's assistant, drafts scenarios and agents from a description. Make also works with MCP in both directions, so other AI tools can call your scenarios.

The pricing is approachable. There's a free plan, paid plans start low, and Make holds the security certifications larger companies ask about, including SOC 2 Type II and ISO 27001.

Where is Make hard for non-technical teams?

Make is easy to start and harder to master. The basics come quickly. Past that, you need to understand how data is mapped from one module to the next, and reviewers mention it. One Capterra reviewer wrote that the advanced features and Make's own terminology "can be challenging for beginners."

A prompt can draft a scenario for you, but someone still has to own it. When an app changes or a step fails, that person opens the canvas and works out which module broke.

Knowledge is set up agent by agent. Each Make agent gets its own instructions and its own knowledge files. That works well for a focused job, like answering questions from a policy document. It's less suited to the kind of knowledge that lives across a year of email, chat and CRM notes. We didn't find a company-wide memory that every scenario and agent shares.

Cost takes some attention. Make bills in credits. A normal step costs one credit, and AI steps can cost more depending on how much text they process. If you connect your own AI provider, Make charges the credit and the provider sends you a separate bill for the model.

How is AgentOS different?

AgentOS starts with what your company knows, not with a canvas. You connect your tools once: Gmail, Slack, Google Drive, your CRM. AgentOS keeps one shared, current picture of the business from them.

Then you describe the job. Something like "every Friday, pull this week's client emails and draft a status update for each account." Otto, the agent built into AgentOS, builds the workflow, runs it on schedule and checks in when a decision needs you. Nobody maps fields or wires up branches.

Because every workflow draws on the same knowledge, the second one is as easy as the first. There are no files to upload for each new job. When a workflow wants to change something important in one of your systems, it waits for a person to approve it.

That knowledge isn't locked inside AgentOS. Claude, ChatGPT and Cursor can connect to it over MCP, and you get one view of what your AI use costs across all of them.

Should you choose Make or AgentOS?

Choose Make if someone on your team likes building and wants to see and control every step, if your automations are mostly app-to-app, or if you need a specific connection from Make's large library.

Choose AgentOS if the people who need the workflows won't build them, if the work depends on knowing your clients and your history, or if your team already uses several AI tools and you want them working from the same knowledge.

They can also work side by side. Make can keep running the app-to-app automations it's good at, while AgentOS holds the company knowledge your AI tools work from.

What about other Make alternatives?

Lists of Make alternatives usually name other builders: Zapier, n8n, Workato, Pipedream and Gumloop. We've compared two of them directly, in AgentOS vs Zapier and AgentOS vs n8n. If you want an AI agent that handles tasks for you, see our look at Lindy alternatives.

If your real problem is that each tool knows a different slice of the business, read what a context layer is, or how to manage AI costs across several tools.

Questions people ask

Is AgentOS a Make alternative?

Yes, for teams that want workflows without building them on a canvas. In AgentOS you describe a workflow in plain language and it runs on your company's shared knowledge. Make gives you more step-by-step control and a larger library of app connections.

Is Make hard to learn?

The basics are easy. More complex scenarios take time, because you need to understand how data is mapped between modules. Reviewers on Capterra rate Make highly and also mention that its advanced features and terminology can be challenging for beginners.

Is Make free?

Make has a free plan. As of October 7, 2026, its pricing page lists up to 1,000 credits a month and 2 active scenarios on Free, with paid plans starting at $12 a month for 10,000 credits.

What is a credit in Make?

A credit is Make's billing unit. For most apps, one step in a scenario uses one credit. AI steps that use Make's own AI provider cost one credit plus more based on how much text is processed.

Does Make have AI agents?

Yes. Make AI Agents can be given instructions, knowledge files and tools, including other scenarios and MCP tools. Make's pricing page still labels them as beta, as of October 7, 2026.

Can I use Make and AgentOS together?

Yes. Make can keep running app-to-app automations, while AgentOS holds the shared company knowledge that your AI tools and agents work from.

AgentOS is free to try. Connect two or three of your tools, describe one workflow you run every week, and see how it handles it.

Try AgentOS free

Sources

AgentOS is built by Devcore. Found something out of date on this page? Tell us at tryagentos.net and we'll fix it.