If you've started exploring AI automation, you've probably come across two things that sound similar but work very differently: Make (formerly Integromat) and AI agents. Both promise to save you time and eliminate manual work. Both involve some level of automation. But confusing the two — or defaulting to one when you need the other — is one of the most common (and costly) mistakes growing businesses make. Understanding the difference isn't just a technical nicety. It directly affects how much time you save, how much you spend, and whether your automation actually holds up when real-world complexity kicks in.
What Make Does (and Does It Well)
Make is a workflow automation platform. You build "scenarios" — visual flowcharts that connect your apps and tell them to pass data from one to another based on triggers and rules. When a new lead fills in your website form, Make can automatically add them to your CRM, send a welcome email, notify your sales team in Slack, and create a task in your project management tool. All within seconds, without anyone lifting a finger.
This is rule-based automation. Every step is defined in advance. If this happens, do that. It's deterministic — meaning the output is predictable because you've mapped every possible path. Make connects to over 1,500 apps and is genuinely powerful for structured, repetitive tasks where the inputs and outputs are consistent.
The ROI here is real and measurable. A small consultancy handling 50 new client enquiries per month might spend 15 minutes manually routing each one — that's 12.5 hours a month, or roughly £500 in staff time at a modest hourly rate. A Make workflow can handle this in seconds, paying for itself (Make's plans start at around £9/month) within the first day of deployment.
Where Make struggles is when the task requires judgment. What if an incoming email is ambiguous? What if a customer complaint needs to be categorised based on nuance rather than keywords? What if the data doesn't fit the template you built? Rule-based automation breaks, stalls, or routes things wrongly. That's where AI agents come in.
What AI Agents Actually Are
An AI agent is a system built around a large language model (like GPT-4) that can reason, make decisions, and take actions — not just follow a fixed script. Where Make follows a flowchart you've drawn in advance, an AI agent can interpret instructions, assess a situation, and decide what to do next based on context.
Think of it this way: Make is a very reliable machine. An AI agent is more like a capable junior employee who understands what you're trying to achieve and can figure out the right steps to get there, even when things don't go exactly to plan.
In practical terms, an AI agent might read an incoming support email, understand that it's a billing dispute (not a technical issue), pull the relevant invoice from your accounting software, draft a personalised response, and flag it to the right team member — all without you defining every possible scenario in advance. It handles the judgment layer that pure automation can't.
A legal support firm using an AI agent to triage incoming client queries reported saving over 3 hours per day across their admin team — roughly 60 hours a month — by eliminating the manual reading, categorising, and routing of emails that previously required human review.
When to Use Make, When to Use an AI Agent, and When to Use Both
The clearest way to choose is to ask one question: does this task require judgment, or just execution?
Use Make when:
- You're connecting tools with clean, structured data (form submissions, calendar events, CRM updates)
- Every step and condition can be defined in advance
- Speed and reliability matter more than flexibility
- You're on a tight budget — Make is significantly cheaper to run at scale than AI-powered tools
Use an AI agent when:
- The inputs are unstructured (emails, documents, voice transcripts, customer messages)
- You need the system to classify, summarise, or interpret before acting
- Responses need to feel personalised or contextual
- The task involves multiple decision points that would take dozens of Make branches to replicate (and would still be brittle)
The most powerful setups use both together. A growing e-commerce brand, for example, might use an AI agent to read and classify incoming customer emails — distinguishing between order queries, complaints, and return requests — and then hand off to Make to execute the appropriate workflow: updating the order management system, sending the right templated reply, alerting the fulfilment team. The AI handles the thinking. Make handles the doing. Together, they cover what neither could do alone.
This hybrid approach is increasingly how serious automation is being built. According to surveys of operations teams using AI tools in 2024, businesses that combine AI agents with structured workflow automation report 40% faster resolution times on customer-facing processes compared to those using either approach alone.
The Practical Cost and Complexity Comparison
It would be misleading to suggest AI agents are always the answer. They come with real trade-offs.
Cost: Make can automate a high-volume workflow for £20–50/month. Running an AI agent that processes hundreds of inputs daily — each requiring an LLM call — can cost significantly more depending on volume, often £100–400/month or more at scale. For simple, repetitive tasks, that cost isn't justified.
Setup complexity: Make's visual builder is genuinely accessible to non-developers. Building a robust AI agent typically requires more expertise — defining the agent's goals, connecting it to the right tools, testing how it handles edge cases, and monitoring it over time. This is where an agency partner adds real value.
Reliability: Make, once built correctly, is extremely reliable. AI agents can occasionally misinterpret inputs or take unexpected actions, which means human-in-the-loop design (where a human reviews certain outputs before they're actioned) is often wise when you're starting out.
A good rule of thumb: if you can draw a simple flowchart of the task with fewer than ten decision points, Make will probably handle it cleanly and cheaply. If explaining the task to a colleague would require more than two paragraphs, you likely need an AI agent.
Conclusion
Make and AI agents aren't rivals — they solve different problems. Make is your best tool for fast, reliable, cost-effective automation of structured, predictable tasks. AI agents are what you reach for when human-like judgment is part of the job. The businesses seeing the biggest gains from automation right now aren't choosing one over the other. They're learning where each belongs, and building systems where the two work in concert. Getting that architecture right from the start saves weeks of rework and ensures your automation still functions when the real world refuses to stay neat and predictable.