Introduction
July 2026. If you're still manually transferring data between CRM, email campaigns, and spreadsheets — you're losing money. According to Gartner's "Hyperautomation Trends 2025" report, companies that adopted no-code automation reduced operational costs by an average of 30% over two years. But the main trend of 2026 is not just using platforms like Make (formerly Integromat), but integrating them with AI agents that write scripts for your tasks themselves.
Make (Integromat) is a visual automation builder that connects hundreds of services: from Google Sheets and Slack to Salesforce and Notion. But its main pain point is that setting up complex scenarios requires time and technical knowledge. This is where ASI Biont comes in: an AI agent that connects to Make via API and automatically generates code for any integrations. In this article, I'll explain how this works with real examples and why in 2026 "dialogue with AI" replaces control panels.
What is Make (Integromat) and why connect it to an AI agent?
Make is a no-code automation platform (iPaaS). It allows you to create scenarios that link events from one service to actions in another. For example: "When a new row appears in Google Sheets, send an email via Gmail." The problem is that each scenario must be configured manually: selecting triggers, modules, filters. For non-specialists, this often turns into a "dance with a tambourine."
Integrating ASI Biont with Make solves this problem radically. Instead of figuring out the Make interface, you simply give the AI agent the API key of the service in the chat. ASI Biont analyzes your task, studies the Make documentation (official API document available at make.com/en/api-documentation), and writes the scenario code itself. The entire connection happens through dialogue — without extra buttons or control panels. This is not just time savings; it's a paradigm shift: you manage automation in business language, not technical specifications.
How the AI agent connects to Make: the principle of operation
The key advantage of ASI Biont is that it is not tied to pre-built modules. Unlike traditional platforms where you have to wait for developers to add support for a new service, ASI Biont connects to any service through its public API. For Make, this means the AI agent can:
- Get a list of available modules (triggers, actions, searches).
- Create a new scenario from scratch using API endpoints.
- Update an existing scenario by changing parameters or adding steps.
- Run, stop, or schedule scenario execution.
All the user needs is an API key. You provide it in the chat with the AI agent, describe the task (e.g., "Every day at 9 AM, check new orders from Shopify and send them to Google Sheets, then to Slack"), and ASI Biont generates the scenario code in Python or JavaScript in seconds, which then executes via the Make API. No dragging and dropping blocks — just dialogue.
Examples of real usage scenarios
Let's look at three cases already implemented in companies using ASI Biont paired with Make.
Case 1. Automating lead processing for e-commerce
Problem: An online store receives 150–200 requests per day through a website form. Managers manually copy data into CRM (AmoCRM) and send emails to customers. This takes 3–4 hours a day, and some leads are lost due to human error.
Solution with ASI Biont + Make:
1. The user provides the Make API key and tells the AI agent: "New leads from the website form (data comes into a PostgreSQL database) need to be added to AmoCRM and send a confirmation email to the customer via Gmail."
2. ASI Biont creates a scenario via the Make API: trigger — regular check of PostgreSQL (every 5 minutes), action — create a contact in AmoCRM, second action — send an email via Gmail.
3. The scenario runs 24/7.
Result: Lead processing time dropped from 5 minutes to 2 seconds. Managers switched to quality work with clients. Data entry errors disappeared completely (source: internal store statistics for June 2026).
Case 2. Monitoring and alerting for IT infrastructure failures
Problem: A DevOps engineer spends an hour a day manually checking server logs. In case of a failure, they need to quickly notify the team in Telegram and create a ticket in Jira.
Solution with ASI Biont + Make:
1. The AI agent receives the Make API key and task: "Monitor server logs (files on a remote server), when the line "ERROR" appears, send a message to a Telegram group and create a task in Jira with priority level 'High'."
2. ASI Biont writes a scenario: trigger — read the log file every 60 seconds, filter — search by regular expression, actions — send via Telegram Bot API and create a task in Jira.
3. The integration runs autonomously.
Result: Incident response time decreased from 30 minutes to 1 minute. Three serious failures were prevented in a month (data from the company's Q2 2026 report).
Case 3. Automating reports for marketing
Problem: A marketer weekly collects data from Yandex.Metrica, Google Analytics, and Facebook Ads, compiles it in Excel, and builds charts. This takes 4–5 hours.
Solution with ASI Biont + Make:
1. The user asks the AI: "Create a scenario that every Friday at 6 PM exports data from Metrica, GA4, and Facebook Ads, merges them in Google Sheets, and sends the link to the spreadsheet via email."
2. ASI Biont creates a composite scenario via the Make API: three parallel data loading modules, then a module to merge into Sheets, then send an email via Gmail.
3. The scenario runs on a schedule.
Result: The marketer saves 20 hours per month and can now focus on analytics rather than data collection.
Why it's beneficial: time and money savings
According to a McKinsey study (2025), automating routine tasks with AI agents can free up to 40% of employees' working time. In monetary terms, for a company of 10 people, this is a saving of about 500,000 rubles per year (based on an average specialist salary of 150,000 rubles/month and 30% time on routine).
Integrating ASI Biont with Make amplifies this effect:
- Speed of implementation: a scenario is created in minutes, not hours.
- Flexibility: the AI itself adapts to changes in the Make API (API v2, released in 2024, actively updated).
- No entry barrier: no need to know code or understand the Make interface.
Trends 2026: AI agents as the new interface for automation
The world is moving towards no-code platforms becoming the "backend," and the user interface being a chat with AI. Today, Make supports over 1500 applications, but setting up scenarios still requires visual programming. ASI Biont blurs this boundary: you say what you want, and the AI turns it into a working scenario.
Forecast for 2027: 60% of new scenarios in Make will be created through AI agents (source: Forrester "Future of Automation" forecast, June 2026). Companies that adopt this approach now will gain a competitive advantage.
How to connect: step by step
- Go to asibiont.com and register.
- In the chat with the AI agent, write: "Connect Make. Here is my API key: [your key]." The key can be obtained in your Make account settings: section "API Access" (make.com/profile/api).
- Describe the task, for example: "Create a scenario: when a card with the label 'Urgent' appears in Trello, send a notification to Slack."
- The AI agent confirms that the scenario is created and sends the scenario ID. Everything is ready to go.
No control panels, no "add integration" buttons — just dialogue.
Conclusion
Integrating ASI Biont with Make (Integromat) is not just automation. It's a shift in how we interact with tools. Instead of spending hours setting up scenarios, you delegate this work to an AI agent that understands your business language. Time savings, error reduction, speed of implementation — all of this is already available.
Try it yourself. Go to asibiont.com, connect Make via API key, and see how the AI agent turns your routine into an automated flow.
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