If you've started exploring automation for your business, you've probably come across two terms that get thrown around a lot: Make (formerly Integromat) and AI agents. They both promise to save you time. They both involve connecting tools and automating tasks. But they're fundamentally different things — and using the wrong one for the wrong job is like hiring a filing clerk to negotiate a contract. Understanding the distinction will help you spend your automation budget wisely and actually solve the problems that are costing you time and money.
What Make Does (and Does Well)
Make is a workflow automation platform. Think of it as a very sophisticated set of "if this, then that" rules. You build visual flowcharts — called scenarios — that connect your apps and tell them what to do when a specific trigger happens.
For example: When a new lead fills out your contact form → add them to your CRM → send a welcome email → notify your sales team in Slack. That sequence runs the same way, every single time, without deviation.
Make is exceptional at this kind of structured, predictable work. It handles data reliably, runs 24/7, and integrates with over 1,500 apps. For many businesses, it eliminates hours of manual copy-pasting each week. A mid-sized recruitment firm, for instance, might use Make to automatically parse incoming CVs submitted via email, create candidate records in their ATS, and schedule an intake call — saving their coordinators roughly 45 minutes per application and eliminating the data-entry errors that were previously causing candidates to fall through the cracks.
The catch? Make only does exactly what you tell it to do. If something unexpected happens — the email arrives in an unusual format, a field is missing, the next step requires a judgement call — the scenario either fails or produces a wrong result. It has no ability to reason, adapt, or handle ambiguity.
What AI Agents Actually Are
An AI agent is a different beast entirely. Where Make follows a fixed script, an AI agent can think through a task, make decisions, and adapt based on what it finds. It combines a large language model (the reasoning engine) with the ability to take actions — browsing the web, reading documents, sending emails, querying databases, updating tools — based on what the situation requires.
The key difference is autonomy and judgement. Ask an AI agent to "review all the support tickets that came in overnight and draft a personalised response to each one, flagging any that mention a refund request for human review" — and it can do that. It reads each ticket individually, understands the context, crafts a relevant reply, and exercises discretion about escalation. A Make scenario can't do any of that, because the content and required response are different every time.
AI agents are also capable of multi-step reasoning. They can work backwards from a goal: "I need to prepare a briefing on this client before tomorrow's meeting" might involve searching your CRM for past interactions, pulling recent emails, checking the client's LinkedIn for news, and synthesising it all into a two-page summary — without you specifying each individual step.
The trade-off is that agents are less deterministic. They're solving novel problems every time, which means occasional errors and a need for human oversight on high-stakes outputs.
How They Work Together in Practice
Here's where it gets interesting: Make and AI agents aren't competitors. For sophisticated workflows, they complement each other beautifully. Make handles the structured plumbing — moving data, triggering events, connecting systems reliably — while AI agents handle the parts that require intelligence.
Consider a growing consultancy managing client onboarding. Their current process involves a new client signing a contract, after which someone manually sends a welcome email, creates a project folder, sets up a Slack channel, briefs the account manager, and schedules a kick-off call. With Make alone, you can automate steps one to four — the structured, rule-based parts. But the welcome email still sounds templated, and the account manager briefing still requires someone to pull together context from the CRM, the proposal document, and past emails.
Add an AI agent to that workflow, and now Make detects the signed contract and triggers the agent. The agent reads the contract, reviews the CRM history, checks the proposal for scope and budget, and writes a genuinely personalised welcome email and a detailed account manager briefing — all within minutes of the signature landing. What used to take a coordinator 90 minutes per client now takes under five.
That consultancy could realistically onboard 3x as many clients without adding headcount, at a combined tooling cost of roughly £150–£300 per month for both platforms.
When to Use Which (A Practical Decision Guide)
The simplest way to decide which tool fits your situation is to ask one question: Is the task the same every time, or does it require judgement?
Use Make when:
- The inputs and outputs are predictable and structured
- You're connecting two or more apps with a clear trigger-and-action relationship
- Speed and reliability matter more than flexibility
- You want something that runs in the background without supervision
- Examples: syncing form submissions to a CRM, sending invoice reminders, routing support tickets by keyword, posting social content on a schedule
Use an AI agent when:
- The content varies and requires reading, interpreting, or generating text
- The task involves decisions that depend on context ("is this complaint serious enough to escalate?")
- You're currently doing knowledge work manually — summarising, drafting, researching, analysing
- A human is currently required because the task needs judgement
- Examples: drafting personalised emails, summarising meeting notes, qualifying leads based on conversation history, answering complex customer queries
Use both together when:
- A structured trigger (new order, inbound email, signed document) kicks off a process that then requires intelligent handling
- You want reliable automation pipelines with AI handling the "thinking" steps in the middle
The cost difference is worth noting. Make scenarios can be built for as little as £20–£50 per month depending on usage. AI agent tools — whether through platforms like n8n with GPT integration, or dedicated tools like Relevance AI — typically add £50–£200 per month on top of that, depending on volume. But the labour they replace often costs far more: at £25/hour for a coordinator, saving even five hours a week justifies the investment in under a month.
Conclusion
Make is your reliable, rules-based workhorse — perfect for the repetitive, structured tasks that clog up your team's day. AI agents bring the intelligence layer: they read, reason, and respond in ways that fixed automation simply can't. For most growing businesses and professional service firms, the right answer isn't one or the other — it's knowing where each tool belongs in your workflow. Start by mapping out which of your manual tasks are purely mechanical and which require a judgement call. That distinction alone will tell you exactly where to invest your automation budget first.