If you've started exploring automation for your business, you've probably come across two terms that seem to do similar things: Make (formerly Integromat) and AI agents. Both promise to save you time and reduce manual work — but they operate in fundamentally different ways, and choosing the wrong one for the job can leave you frustrated and no better off than when you started. Understanding the distinction isn't just a technical exercise. It directly affects how much time you save, how much you spend, and whether your automation actually holds up when real-world messiness gets in the way.
What Make Does (And Does It Well)
Make is a workflow automation platform — think of it as a very sophisticated set of digital dominoes. You define the rules upfront: if this happens, do that. For example, "when a new order comes in through Shopify, add the customer to Mailchimp, send a confirmation email, and log the sale in Google Sheets." Every step is explicit, every condition is pre-programmed, and the system follows your instructions to the letter every single time.
This predictability is Make's superpower. For structured, repetitive tasks with clear inputs and outputs, it's fast to set up, reliable to run, and relatively cheap — plans start from around $9 per month, and a single well-built scenario can save a small team 5–10 hours a week on data entry alone.
Where Make starts to struggle is when the work requires judgment. If an incoming customer email could be a complaint, a refund request, a general enquiry, or a glowing testimonial — and each needs a different response — Make needs a separate branch built for every possible case. Miss one, and the automation breaks or routes things incorrectly. The more exceptions and edge cases your process has, the more brittle your Make scenario becomes.
What AI Agents Actually Are
An AI agent is something different in kind, not just in degree. Rather than following a fixed flowchart, an AI agent can reason about a situation, decide what to do next, and use tools — email, calendars, CRMs, databases — to take action. It doesn't need every possible scenario mapped out in advance because it can interpret context and make decisions on the fly.
Think of the difference this way: Make is an excellent assembly line worker who follows instructions perfectly. An AI agent is more like a junior employee who understands the goal, reads the situation, and figures out the steps needed to get there.
In practice, this means AI agents excel at tasks involving natural language, ambiguous inputs, or multi-step reasoning. Summarising a 40-page contract and flagging unusual clauses. Triaging a support inbox and drafting personalised responses. Researching a prospect, cross-referencing your CRM, and drafting a tailored outreach email — all in one go. These are tasks where the input varies wildly and the "right" output requires understanding, not just pattern-matching.
The trade-off is that AI agents cost more to run (typically priced per token or API call), can occasionally produce unexpected outputs, and require some oversight, particularly for high-stakes decisions. They're powerful — but not a set-and-forget tool in the same way a Make scenario is.
A Real Business Example: How a Law Firm Uses Both
A mid-sized employment law firm in Manchester illustrates how Make and AI agents can work together rather than compete.
Before automation, their intake process was a headache. New enquiries arrived by email, web form, and phone message transcription — all landing in a shared inbox. A paralegal spent around 90 minutes every morning sorting them, logging details into the CRM, and assigning them to the right solicitor. Urgent matters sometimes sat unread until late morning.
They now run a two-layer system. Make handles the structured, trigger-based work: when a web form is submitted, it instantly creates a CRM record, timestamps the enquiry, and sends an acknowledgement email to the client. That takes under two seconds and happens at 2am just as reliably as 9am.
The AI agent layer handles what Make can't. It reads the full content of each enquiry — whether it came from a form, a forwarded email, or a transcribed voicemail — assesses the likely case type, urgency, and complexity, and routes it to the appropriate solicitor with a brief summary already written. If the enquiry is vague or emotionally sensitive, the agent flags it for a human to review before any automated response goes out.
The result: intake processing time dropped from 90 minutes to under 10 minutes of human review per day. The firm estimates they recovered roughly 7 hours of paralegal time per week — time now spent on billable work. At an average billing rate of £150 per hour, that's a potential revenue impact of over £50,000 per year from a single process change.
How to Decide Which One You Need
The practical rule of thumb is this: if you can write down every possible input and the correct response to each, Make is probably the right tool. If the inputs are unpredictable, involve natural language, or require reading between the lines, you likely need an AI agent — or a combination of both.
Use Make when:
- You're connecting two or more apps with structured data (forms, spreadsheets, CRMs)
- The trigger and outcome are always the same type of thing
- You need reliability and low cost above all else
- You're automating volume tasks: syncing records, sending notifications, generating reports
Use an AI agent when:
- The input varies significantly (emails, documents, voice transcripts)
- The task requires understanding context or intent
- You need something summarised, classified, or drafted
- You want the automation to handle edge cases without breaking
Use both together when you want the best of each world — Make handles the structured plumbing, while the AI agent handles the thinking. This combination is increasingly common in practices like legal, consulting, and healthcare, where structured workflows meet highly variable human communication.
A good starting point is to map out one process that currently costs you more than three hours a week in manual effort. Ask yourself: are the inputs always the same shape, or do they vary? That single question will point you toward the right tool almost every time.
Conclusion
Make and AI agents aren't rivals — they're different tools built for different kinds of work. Make is your best choice for reliable, rule-based automation where speed and low cost matter. AI agents step in where judgment, language, and flexibility are required. The smartest implementations use both: structured automation doing the heavy lifting on predictable tasks, with an AI layer handling the parts that require something closer to thinking. Knowing which is which means you stop paying for complexity you don't need — and stop settling for fragile automations that break the moment something unexpected happens.