You've probably heard both terms thrown around in the same breath — Make (formerly Integromat) and AI agents. Maybe you've even used Make already to connect your CRM to your email tool, or to auto-post to social media when a new lead comes in. It works brilliantly for that. But then someone mentions an "AI agent" and suddenly the lines blur. Are they the same thing? Is one better than the other? The honest answer is: they solve different problems, and confusing them is one of the most common reasons automation projects either fail or deliver far less than they should. Here's how to tell them apart — and how to pick the right tool for the job.
What Make Actually Does (and Does Well)
Make is a workflow automation platform. Think of it as a very sophisticated series of "if this, then that" rules. When a trigger fires — a new row in a spreadsheet, a form submission, a payment hitting Stripe — Make executes a predetermined sequence of steps. It connects apps, moves data between them, and applies logic you've defined in advance.
This is genuinely powerful. A busy dental clinic, for example, might use Make to automatically send appointment reminder SMS messages 24 hours before a booking, update their patient spreadsheet when a form is completed, and notify the front desk via Slack when a new enquiry arrives — all without a single person touching a keyboard. Set it up once, and it runs reliably in the background, potentially saving 8–10 hours of admin per week.
Make excels when:
- The process is predictable and repeatable — the same input always produces the same output
- You're moving structured data between known systems (CRM → email tool → spreadsheet)
- Speed and reliability matter more than nuance — you want it done fast, every time, without variation
- The trigger and the outcome are both clearly defined before you build anything
Make is not designed to think. It follows instructions. That's a feature, not a limitation — until you need something to make a judgement call.
What AI Agents Actually Do (and Do Differently)
An AI agent is a system that can perceive its environment, reason about what needs to happen, and take action — often without a rigid pre-set sequence. Unlike Make, which follows a flowchart you've drawn, an AI agent can interpret unstructured information (like the content of an email), decide what the appropriate next step is, use tools at its disposal (search, write, send, retrieve), and adapt if something unexpected happens.
The critical difference: AI agents handle ambiguity. Make handles predictability.
Imagine a legal consultancy receiving a new client enquiry email. Make could detect the incoming email and log it in the CRM. But an AI agent could read the email, classify the enquiry type (employment law, contract dispute, property), draft a tailored initial response drawing on past similar cases, flag it for the relevant partner, and update the CRM with a summary — all without a human scripting exactly what to do for every possible variant of that email.
That's not a workflow. That's reasoning. And it's where the ROI gets interesting.
Firms using AI agents for email triage and initial client response report cutting response times from 4–6 hours down to under 10 minutes, while maintaining accuracy rates above 90% on categorisation. For a consultancy billing at £200/hour, recovering even two hours of partner time per day translates to roughly £2,000 in recaptured billable capacity weekly.
AI agents work best when:
- The inputs are unstructured or variable (emails, documents, voice transcripts, customer messages)
- The process requires interpretation — reading between the lines, matching context, making a call
- You need the system to use multiple tools in sequence based on what it discovers
- The task looks different each time it runs
How They Work Together (The Real Power Move)
Here's what most guides miss: Make and AI agents aren't competitors. They're teammates, and the smartest setups use both.
Make handles the structured plumbing — the triggers, the data movement, the reliable if-then scaffolding. The AI agent sits inside that structure and handles the parts that require judgement. You get the reliability of Make with the intelligence of AI, and neither component is doing a job it's not built for.
A practical example: a boutique recruitment firm automated their entire candidate screening pipeline using exactly this combination. Make monitors their inbox and applicant tracking system, detects when a new CV arrives, and triggers the process. An AI agent then reads the CV, scores it against the job brief, writes a personalised response to the candidate, and drafts a shortlisting note for the hiring manager. Make then routes that note to the correct Slack channel and logs the outcome in the spreadsheet. End to end, a process that previously took a recruiter 25 minutes per candidate now runs in under 90 seconds, with the recruiter only involved when a candidate clears the shortlisting threshold.
At 40 applications per week, that's over 15 hours of recruiter time recovered — the equivalent of nearly two full working days. At a blended recruiter cost of £35/hour, that's £525 in labour savings weekly, or roughly £25,000 annually, from one workflow.
How to Decide Which One You Need
The fastest way to work out what you're dealing with is to ask one question: Does this process ever require a human to read something and make a decision based on what it says?
If no — if the same input always produces the same output and you're just moving data — start with Make. It's faster to build, easier to maintain, and cheaper to run. Most appointment scheduling, invoice chasing, CRM updates, and notification workflows live here.
If yes — if someone has to interpret an email, summarise a document, classify a request, or draft a response that varies depending on context — you need an AI agent, either on its own or built into a Make workflow.
A few useful rules of thumb:
- New to automation? Start with Make. Get the structured workflows running first. You'll find the AI agent opportunities once you've mapped your processes.
- Already running Make workflows but still have humans doing triage, drafting, or classifying? That's where AI agents plug in.
- Building from scratch with complex, document-heavy processes? You probably need both from day one.
The cost entry point matters too. Make's business plan starts around £16/month. AI agents built on models like GPT-4o typically cost based on token usage — for a small firm handling 50–100 tasks per day, expect to budget £50–£200/month depending on complexity. Neither is prohibitive. Both should pay for themselves quickly when applied to the right problems.
Conclusion
Make is the backbone. AI agents are the brain. Neither replaces the other, and choosing one when you need the other is where most automation efforts stall. The key is matching the tool to the nature of the task: predictable and structured goes to Make; ambiguous and judgement-heavy goes to the AI agent. Once you see your workflows through that lens, the right architecture becomes obvious — and the time and cost savings follow quickly behind.