How to Build AI Agents in Make.com

This how-to guide explains, step by step, how to design, configure, and deploy AI agents in make.com so you can automate tasks, connect tools, and streamline your workflows with practical, real-world scenarios.

Following the official example use case, you will learn how to create an AI agent that collects user input, calls external data sources and services, and responds with structured, useful results inside your automations.

What You Need Before Using Make.com AI Agents

Before you start configuring AI agents in make.com, prepare these essentials:

An active make.com account with access to the AI Agent tools.
Basic understanding of scenarios and modules in the platform.
Access credentials (API keys or app connections) for any external tools you plan to call.
A clear task or use case your AI agent should solve, such as summarizing content, generating responses, or orchestrating multiple services.

Defining the purpose of your automation at the start will help you choose the right structure and setup for your AI agent.

Understand the Make.com AI Agent Use Case

The example use case from the make.com documentation demonstrates how an AI agent can coordinate several steps:

Receiving a user request as input.
Analyzing that request using a language model.
Deciding which external tools to call.
Executing those calls through scenario modules.
Returning a structured, human‑readable response.

You can review the full reference scenario on the official help page at this make.com AI agent use case.

Step 1: Plan Your Make.com AI Agent

Start by defining the objective of your automation and the role of the agent in make.com:

Define the user goal. Clearly state what a user expects, for example: “Provide a curated list of products based on preferences” or “Summarize knowledge from several data sources.”
List required tools. Identify which external apps or services the agent must call, such as databases, CRMs, or knowledge bases.
Choose the desired output format. Decide how responses should look: bullet lists, structured text, JSON-like structures, or short messages.
Clarify limitations. Note what the agent must not do, such as performing destructive actions or revealing internal data.

This planning step ensures your make.com agent configuration stays focused and predictable.

Step 2: Create an AI Agent in Make.com

Once you have a clear goal, you can create a new AI agent inside make.com.

Open the Make.com AI Agent Interface

Log in to your make.com dashboard.
Navigate to the AI or Agents section (as described in the platform’s help resources).
Click the option to create a new AI agent.

You will see configuration fields that define how the agent behaves and how it is used in your scenarios.

Configure Core Agent Settings in Make.com

Set the core properties that control behavior:

Name and description: Use a descriptive name that explains the role of the AI agent, such as “Product Suggestion Agent” or “Knowledge Summary Agent.”
System instructions: Provide clear, concise directions that define the agent’s personality, allowed actions, tone, and output structure.
Input expectations: Document what kind of prompt or parameters the agent receives from your scenario modules.
Output structure: Instruct the agent to respond in a consistent format that matches the modules which will process its output.

Well‑written instructions greatly improve the reliability of your automations in make.com.

Step 3: Connect Tools and Data Sources

The power of an AI agent in make.com comes from its ability to call external tools through scenarios.

Identify Tools Your Make.com Agent Will Use

Based on the use case example, an agent may need to:

Call web APIs for fresh data.
Query knowledge bases or internal documents.
Access spreadsheets or databases.
Trigger actions in CRMs, project tools, or messaging platforms.

Each of these capabilities is represented as a module or set of modules in your scenario.

Map Agent Actions to Make.com Modules

In the make.com scenario editor, connect your agent to tools as follows:

Create or open a scenario.
Add modules for each external service you want the agent to call.
Define input and output fields so that the agent’s generated instructions can control those modules.
Use routing or conditional logic to handle different types of agent decisions.

The example use case shows how the AI agent can decide which branch or tool to call, then send back a summarized result.

Step 4: Integrate the AI Agent into a Scenario

After setting up the agent and tools, integrate everything into a complete flow inside make.com.

Design the Scenario Flow in Make.com

Set the trigger. Choose how the scenario starts, such as a webhook, form submission, message, or scheduled run.
Pass user input to the agent. Map the incoming data (text, parameters, or context) into the AI agent input fields.
Let the agent decide actions. Configure the agent to output instructions or parameters that determine which modules should run.
Call the relevant modules. Use the agent’s output to drive API calls, database queries, or other actions.
Aggregate the results. Collect data from all called tools for the final response.

This approach follows the example demonstrated in the official use case, where the agent orchestrates multiple calls behind the scenes.

Return a Structured Response

Finally, configure the last steps in your make.com scenario to format and deliver a clear response to the user:

Transform or format the collected data into readable text.
Include key details and avoid unnecessary noise.
Send the result back through email, chat, an API response, or another channel.

In your agent’s instructions, specify the exact structure required so downstream modules can safely parse and display the output.

Step 5: Test and Refine Your Make.com AI Agent

Thorough testing is essential for stable automations in make.com.

Run Controlled Tests

Use test inputs that match your real user requests.
Check whether the agent chooses the correct tools in the scenario.
Verify that each external module receives valid parameters and returns expected data.
Inspect the final response for clarity, correctness, and completeness.

If results are inconsistent, adjust the agent’s system instructions or refine the mapping between outputs and scenario modules.

Optimize Instructions and Workflow

Improve reliability by:

Adding explicit constraints to the agent’s behavior (for example, word limits, formats, or tone).
Implementing error handling modules in the scenario for failed API calls or missing data.
Splitting complex workflows into smaller, focused scenarios that the agent can trigger or coordinate.

Continual refinement will help you get maximum value from AI‑powered workflows in make.com.

Use Make.com AI Agents in Real Projects

Once your AI agent works as expected, you can roll it out in real environments:

Embed it behind forms, widgets, or internal tools.
Connect it to your CRM, help desk, or knowledge base to support customers or teams.
Combine it with analytics modules to track performance and outcomes.

For help designing production‑ready architectures or complex automations, you can consult specialists, such as the team at Consultevo, who focus on workflow automation and AI orchestration.

Next Steps with Make.com AI Agents

AI agents in make.com allow you to orchestrate multiple tools, integrate data sources, and return tailored responses in a single automated flow. By following the official AI agent use case, you can:

Plan clear objectives and constraints.
Create and configure a robust agent.
Connect external tools through scenario modules.
Test, refine, and safely scale your automation.

Use the reference guide on the official help site at help.make.com AI agent use case alongside this tutorial to implement reliable, production‑ready AI workflows.

Need Help With Make.com?

If you want expert help building, automating, or scaling your Make scenarios, work with ConsultEvo — certified workflow and automation specialists.

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Make.com AI Agents How-To Guide