If you've started exploring AI automation for your business, you've probably come across two terms that sound similar but work very differently: Make (formerly Integromat) and AI agents. Both can save you time, reduce manual work, and connect your tools together — but confusing one for the other is like hiring a conveyor belt when you need a human assistant, or vice versa. Getting this distinction right means the difference between automation that actually solves your problems and a tech stack that creates new ones.
What Make Does (And Does It Well)
Make is a workflow automation platform. At its core, it watches for a trigger — a new form submission, an incoming email, a Stripe payment — and then executes a fixed sequence of steps you've pre-defined. Think of it as a very reliable, very fast intern who follows a checklist perfectly every single time, without ever improvising.
A practical example: a physiotherapy clinic in Bristol uses Make to handle new patient bookings. When someone submits a booking form, Make automatically adds them to the CRM, sends a confirmation email, creates a calendar event for the therapist, and logs the appointment in a spreadsheet — all within about 30 seconds of the form being submitted. Before automation, a receptionist spent roughly 12 minutes per booking doing this manually. With Make handling 40+ bookings a week, that's 8 hours of admin time recovered every single week.
Make excels when:
- The process is clearly defined and doesn't change
- Every input follows a predictable format
- You need reliability and consistency at scale
- The steps are sequential (if this, then that)
The limitation is equally clear: Make cannot think. If an email arrives in an unexpected format, or a customer asks something ambiguous, Make either fails silently or throws an error. It has no judgement — only rules.
What AI Agents Actually Are
An AI agent is something fundamentally different. Rather than following a fixed script, an AI agent reasons through a task. It can read unstructured information (like a messy customer email), decide what needs to happen, choose which tools or actions to use, and adapt based on context — all without you pre-defining every possible scenario.
Where Make asks "what step comes next?", an AI agent asks "what's the best way to handle this situation?"
Take a legal consultancy as a concrete example. Their intake process used to break down whenever a prospective client sent an email that didn't fit the standard enquiry form. Someone had to read it, figure out what kind of matter it was, check the right partner's availability, draft a tailored response, and log it in the CRM — a task that took 20–25 minutes per enquiry and often sat in someone's inbox for hours. An AI agent now handles this end-to-end: it reads the email, classifies the matter type, pulls relevant partner availability from the calendar tool, drafts a contextually appropriate response, and logs the interaction — in under 90 seconds, with no human in the loop unless the agent flags genuine uncertainty.
AI agents are the right choice when:
- Inputs are unstructured or variable (emails, documents, voice notes)
- The task requires interpretation or judgment
- Different inputs should lead to meaningfully different outputs
- You want the system to handle edge cases without breaking
The Key Differences Side by Side
Understanding where these tools diverge helps you make smarter decisions before you invest time or money into either.
Flexibility vs. predictability. Make is highly predictable — you know exactly what it will do. An AI agent is flexible — it can handle situations you didn't anticipate, but that flexibility requires more careful setup and testing upfront.
Structured vs. unstructured data. Make works brilliantly with structured data: form fields, database rows, webhook payloads. AI agents are built for unstructured inputs: natural language, PDFs, scanned documents, or any information that doesn't arrive in a clean, consistent format.
Cost. Make charges based on the number of operations (steps in a workflow), and a well-built workflow for a small business typically runs between £20–£80/month. AI agent costs vary more widely because they use large language models on each run — expect to pay more per task, often £0.01–£0.10 per complex interaction depending on volume and the model used. For high-volume, simple tasks, Make is usually cheaper. For complex, low-to-medium volume tasks requiring judgment, AI agents justify the cost through the hours they save.
Time to build. A Make scenario for a standard process can be live in a day or two. An AI agent handling nuanced tasks needs more design time — typically a week or more to prompt, test, and refine properly.
Error handling. Make will stop and alert you if something unexpected happens. A well-designed AI agent can reason its way through unexpected situations, though it needs clear guardrails to avoid making decisions it shouldn't.
When to Use Both Together
Here's where it gets genuinely powerful: Make and AI agents aren't competitors. They're complementary layers in a well-designed automation stack.
A growing e-commerce consultancy combined both tools to overhaul their client onboarding. Make handles all the structured, sequential steps — creating the client folder in Google Drive, sending the welcome email sequence, setting up the project in Asana, and generating the initial invoice in Xero. That part is fast, reliable, and costs almost nothing to run.
The AI agent layer handles the parts that require thinking: reading the client's initial brief (which arrives as a free-form document), extracting key goals and constraints, drafting a customised onboarding questionnaire, and flagging any unusual requests or potential scope issues for the account manager to review. What used to take an account manager 3–4 hours per new client now takes about 25 minutes of light review.
The rule of thumb is simple: use Make for the plumbing, use AI agents for the thinking. Map your process end-to-end and ask, at each step, "does this require judgment or just execution?" Judgment steps get an AI agent. Execution steps get Make. Connect them and you have an automation stack that's both fast and smart.
Conclusion
Make and AI agents solve different problems, and the businesses getting the most from automation aren't choosing between them — they're deploying each where it fits. If your bottleneck is repetitive, structured tasks with predictable inputs, Make can start saving you hours this week. If your bottleneck is anything that currently requires someone to read, interpret, and decide — emails, documents, client requests — an AI agent is worth exploring seriously. Start by picking one process that's costing you the most time, identifying whether it needs execution or judgment, and matching the right tool accordingly. That single decision will get you further than any amount of general research.