A professional guide to Make AI Agents, visual scenarios, Maia, Make Grid, MCP, and credit-based automation.
Many automation platforms promise "AI agents," but few let you see what is actually happening inside them. Make (formerly Integromat) is built around a visual canvas where every step, branch, and data flow is visible. That makes it a strong fit for teams that want AI-powered automation without losing control or visibility.
This guide covers what Make offers, how its AI agents work, and how to build a reliable professional workflow.
Make is a visual, no-code automation platform. You build scenarios by connecting modules on a canvas: triggers, app actions, data transformations, routers, and AI steps. According to Make's own announcements, more than 250,000 organizations use it worldwide, and it offers thousands of app integrations. See the official Make integrations directory for the current list.
Make sits between simple "if this then that" tools and developer-first platforms: it is visual enough for non-coders, but flexible enough for complex logic such as routers, iterators, aggregators, and error handling.
Make's new AI Agents were introduced in early 2026 as part of a redesigned experience. An agent inside a scenario combines a few components:
| Component | Role |
|---|---|
| AI model ("brain") | Reasons about the task, using Make's default AI provider or your own connected provider |
| Instructions | Define the agent's role, goal, and rules |
| Tools | Scenarios or modules the agent can call to take action |
| Knowledge | Documents and data the agent can reference |
| Memory/context | Helps the agent stay consistent across steps |
The key idea is that you can build agents that you can see, control, and scale. Because they live inside the same visual canvas as the rest of your automation, you can surround an agent with filters, routers, and approval steps. The official help center includes guides such as creating your first AI agent, building agents for different triggers, and AI agent best practices.
Maia is Make's AI assistant for building. Instead of dragging every module by hand, you describe the workflow in chat and Maia helps create it, including agents inside the workflow. It is a faster starting point for beginners and a time-saver for experienced builders. As with any generated workflow, review each step before turning it on.
Make Grid is an auto-generated visual map of your automation and AI landscape. It shows how scenarios, apps, and agents connect, and supports observability, monitoring, and troubleshooting. This matters as automation grows: when dozens of scenarios depend on each other, you need to see what breaks when one changes. The Make Grid documentation includes tutorials such as how to replace a dependency.
Make also documents support for the Model Context Protocol (MCP), including a Make MCP server and MCP toolboxes. This lets compatible AI clients call Make scenarios as tools, which turns your existing automations into capabilities for external AI assistants. Check the help center for the current setup steps.
The canvas shows data flowing from module to module, which makes workflows easier to understand, review, and hand over to colleagues.
Routers, filters, iterators, aggregators, and built-in functions handle complex branching and data shaping.
Make provides error handlers, retries, and exponential backoff options, which matter when agents call unreliable external APIs.
Execution history and Make Grid help you debug and monitor production automations.
You can use Make's built-in AI provider or connect your own API key for providers such as OpenAI or Anthropic.
Examples include qualifying inbound leads, triaging support emails, or summarizing meeting notes. Narrow tasks are easier to test and trust.
Start with a webhook, scheduled run, email, form submission, or app event. Webhook-based triggers are efficient because they run only when something happens.
Choose your AI provider and write clear instructions using this structure:
Role + Goal + Inputs + Allowed tools + Rules + Output format
Example: "You are a lead-qualification assistant. Review each new form submission, check the company size against our criteria, and return a score from 1 to 5 with a one-sentence reason. Never send emails. Return JSON."
Attach only the scenarios or modules the agent needs, and upload reference documents if it must answer from your content.
Use routers and filters to send results down the right path, validate the output, and escalate uncertain cases to a human. Add an approval step before sensitive actions like sending external messages.
Add error handlers and retries for key modules, and set notifications for failures.
Run past cases, including edge cases and messy data. Check execution logs and refine instructions before activating the scenario.
Use Make Grid to keep track of dependencies as you add more scenarios and agents.
Make moved to a credit-based pricing model in 2025. Credits are consumed as scenarios run, and AI features can use additional credits depending on the provider and usage. Because agents can make multiple model calls per task, monitor consumption early. Plans, credit amounts, and free-tier limits change, so check the official Make pricing page before budgeting.
Make is a strong choice for teams that value visibility. Its canvas makes AI automation understandable, Make Grid helps you manage complexity as you scale, and the AI Agents let you add reasoning without leaving the visual builder. The trade-offs are a learning curve for complex scenarios and credit usage that needs monitoring. If you need self-hosting and deep code control, n8n may fit better. If you want the simplest possible setup, Zapier may be easier. If you want a balance of power, visual clarity, and no-code accessibility, Make deserves a close look.
The best way to evaluate it is to build one small agent scenario end to end. Explore Make and start with a real task.
Make is a visual, no-code automation platform for connecting apps, moving data, and building AI-powered workflows called scenarios. Teams use it for lead routing, support triage, reporting, data syncing, and AI agent workflows. Learn more at make.com.
Make AI Agents are AI-powered components you add inside scenarios. They use an AI model, instructions, tools, and knowledge to reason about a task and take actions through your connected apps. See the Make Help Center for guides.
Maia is Make's AI assistant for building. You describe a workflow in chat, and Maia helps create it, including AI agents inside the workflow. Always review generated steps before activating them.
Make Grid is an auto-generated, interactive map of your automations and AI agents. It helps you visualize dependencies, monitor performance, and troubleshoot as your automation grows. Learn more in the Make Grid documentation.
Yes. Make's documentation covers the Model Context Protocol, including a Make MCP server and MCP toolboxes, so AI clients can use Make scenarios as tools. Features may vary, so check the help center.
Make has offered a free plan with limited usage, and paid plans scale with credit consumption. Plans change, so check the official pricing page for current details.
Make is known for its visual canvas and strong data-handling logic. Zapier is generally simpler for non-technical teams, while n8n offers self-hosting and deeper code customization. The best choice depends on your team's skills, budget, and data requirements.
